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Greetings from the IEEE International Conference on Robotics and Automation (ICRA) in Yokohama, Japan! We hope you’ve been enjoying our short videos on TikTok, YouTube, and Instagram. They are just a preview of our in-depth ICRA coverage, and over the next several weeks we’ll have lots of articles and videos for you. In today’s edition of Video Friday, we bring you a dozen of the most interesting projects presented at the conference.

Enjoy today’s videos, and stay tuned for more ICRA posts!

Upcoming robotics events for the next few months:

RoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDSICSR 2024: 23–26 October 2024, ODENSE, DENMARKCybathlon 2024: 25–27 October 2024, ZURICH, SWITZERLAND

Please send us your events for inclusion.

The following two videos are part of the “ Cooking Robotics: Perception and Motion Planning” workshop, which explored “the new frontiers of ‘robots in cooking,’ addressing various scientific research questions, including hardware considerations, key challenges in multimodal perception, motion planning and control, experimental methodologies, and benchmarking approaches.” The workshop featured robots handling food items like cookies, burgers, and cereal, and the two robots seen in the videos below used knives to slice cucumbers and cakes. You can watch all workshop videos here.

“SliceIt!: Simulation-Based Reinforcement Learning for Compliant Robotic Food Slicing,” by Cristian C. Beltran-Hernandez, Nicolas Erbetti, and Masashi Hamaya from OMRON SINIC X Corporation, Tokyo, Japan.

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specifically, our goal is to enable a collaborative robot or industrial robot arm to perform food-slicing tasks by adapting to varying material properties using compliance control. Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board. However, training the robot in the real world can be inefficient, and dangerous, and result in a lot of food waste. Therefore, we proposed SliceIt!, a framework for safely and efficiently learning robot food-slicing tasks in simulation. Following a real2sim2real approach, our framework consists of collecting a few real food slicing data, calibrating our dual simulation environment (a high-fidelity cutting simulator and a robotic simulator), learning compliant control policies on the calibrated simulation environment, and finally, deploying the policies on the real robot.

“Cafe Robot: Integrated AI Skillset Based on Large Language Models,” by Jad Tarifi, Nima Asgharbeygi, Shuhei Takamatsu, and Masataka Goto from Integral AI in Tokyo, Japan, and Mountain View, Calif., USA.

The cafe robot engages in natural language inter-action to receive orders and subsequently prepares coffee and cakes. Each action involved in making these items is executed using AI skills developed by Integral, including Integral Liquid Pouring, Integral Powder Scooping, and Integral Cutting. The dialogue for making coffee, as well as the coordination of each action based on the dialogue, is facilitated by the Integral Task Planner.

“Autonomous Overhead Powerline Recharging for Uninterrupted Drone Operations,” by Viet Duong Hoang, Frederik Falk Nyboe, Nicolaj Haarhøj Malle, and Emad Ebeid from University of Southern Denmark, Odense, Denmark.

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which a control circuit regulates the magnetic field inside a split-core current transformer to provide sufficient holding force as well as battery recharging. The system is evaluated in an active outdoor three-phase powerline environment. We demonstrate multiple contiguous hours of fully autonomous uninterrupted drone operations composed of several cycles of flying, landing, recharging, and takeoff, validating the capability of extended, essentially unlimited, operational endurance.

“Learning Quadrupedal Locomotion With Impaired Joints Using Random Joint Masking,” by Mincheol Kim, Ukcheol Shin, and Jung-Yup Kim from Seoul National University of Science and Technology, Seoul, South Korea, and Robotics Institute, Carnegie Mellon University, Pittsburgh, Pa., USA.

Quadrupedal robots have played a crucial role in various environments, from structured environments to complex harsh terrains, thanks to their agile locomotion ability. However, these robots can easily lose their locomotion functionality if damaged by external accidents or internal malfunctions. In this paper, we propose a novel deep reinforcement learning framework to enable a quadrupedal robot to walk with impaired joints. The proposed framework consists of three components: 1) a random joint masking strategy for simulating impaired joint scenarios, 2) a joint state estimator to predict an implicit status of current joint condition based on past observation history, and 3) progressive curriculum learning to allow a single network to conduct both normal gait and various joint-impaired gaits. We verify that our framework enables the Unitree’s Go1 robot to walk under various impaired joint conditions in real world indoor and outdoor environments.

“Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions With Application to Multi-Contact Exoskeleton Locomotion,” by Maegan Tucker, Kejun Li, and Aaron D. Ames from Georgia Institute of Technology, Atlanta, Ga., and California Institute of Technology, Pasadena, Calif., USA.

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation in-the-loop learning approach. Further, we leverage discrete time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multicontact walking on the Atalante lower-body exoskeleton.

“Supernumerary Robotic Limbs to Support Post-Fall Recoveries for Astronauts,” by Erik Ballesteros, Sang-Yoep Lee, Kalind C. Carpenter, and H. Harry Asada from MIT, Cambridge, Mass., USA, and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, Calif., USA.

This paper proposes the utilization of Supernumerary Robotic Limbs (SuperLimbs) for augmenting astronauts during an Extra-Vehicular Activity (EVA) in a partial-gravity environment. We investigate the effectiveness of SuperLimbs in assisting astronauts to their feet following a fall. Based on preliminary observations from a pilot human study, we categorized post-fall recoveries into a sequence of statically stable poses called “waypoints”. The paths between the waypoints can be modeled with a simplified kinetic motion applied about a specific point on the body. Following the characterization of post-fall recoveries, we designed a task-space impedance control with high damping and low stiffness, where the SuperLimbs provide an astronaut with assistance in post-fall recovery while keeping the human in-the-loop scheme. In order to validate this control scheme, a full-scale wearable analog space suit was constructed and tested with a SuperLimbs prototype. Results from the experimentation found that without assistance, astronauts would impulsively exert themselves to perform a post-fall recovery, which resulted in high energy consumption and instabilities maintaining an upright posture, concurring with prior NASA studies. When the SuperLimbs provided assistance, the astronaut’s energy consumption and deviation in their tracking as they performed a post-fall recovery was reduced considerably.

“ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch,” by Zhengrong Xue, Han Zhang, Jingwen Cheng, Zhengmao He, Yuanchen Ju, Changyi Lin, Gu Zhang, and Huazhe Xu from Tsinghua Embodied AI Lab, IIIS, Tsinghua University; Shanghai Qi Zhi Institute; Shanghai AI Lab; and Shanghai Jiao Tong University, Shanghai, China.

We present ArrayBot, a distributed manipulation system consisting of a 16 × 16 array of vertically sliding pillars integrated with tactile sensors. Functionally, ArrayBot is designed to simultaneously support, perceive, and manipulate the tabletop objects. Towards generalizable distributed manipulation, we leverage reinforcement learning (RL) algorithms for the automatic discovery of control policies. In the face of the massively redundant actions, we propose to reshape the action space by considering the spatially local action patch and the low-frequency actions in the frequency domain. With this reshaped action space, we train RL agents that can relocate diverse objects through tactile observations only. Intriguingly, we find that the discovered policy can not only generalize to unseen object shapes in the simulator but also have the ability to transfer to the physical robot without any sim-to-real fine tuning. Leveraging the deployed policy, we derive more real world manipulation skills on ArrayBot to further illustrate the distinctive merits of our proposed system.

“SKT-Hang: Hanging Everyday Objects via Object-Agnostic Semantic Keypoint Trajectory Generation,” by Chia-Liang Kuo, Yu-Wei Chao, and Yi-Ting Chen from National Yang Ming Chiao Tung University, in Taipei and Hsinchu, Taiwan, and NVIDIA.

We study the problem of hanging a wide range of grasped objects on diverse supporting items. Hanging objects is a ubiquitous task that is encountered in numerous aspects of our everyday lives. However, both the objects and supporting items can exhibit substantial variations in their shapes and structures, bringing two challenging issues: (1) determining the task-relevant geometric structures across different objects and supporting items, and (2) identifying a robust action sequence to accommodate the shape variations of supporting items. To this end, we propose Semantic Keypoint Trajectory (SKT), an object agnostic representation that is highly versatile and applicable to various everyday objects. We also propose Shape-conditioned Trajectory Deformation Network (SCTDN), a model that learns to generate SKT by deforming a template trajectory based on the task-relevant geometric structure features of the supporting items. We conduct extensive experiments and demonstrate substantial improvements in our framework over existing robot hanging methods in the success rate and inference time. Finally, our simulation-trained framework shows promising hanging results in the real world.

“TEXterity: Tactile Extrinsic deXterity,” by Antonia Bronars, Sangwoon Kim, Parag Patre, and Alberto Rodriguez from MIT and Magna International Inc.

We introduce a novel approach that combines tactile estimation and control for in-hand object manipulation. By integrating measurements from robot kinematics and an image based tactile sensor, our framework estimates and tracks object pose while simultaneously generating motion plans in a receding horizon fashion to control the pose of a grasped object. This approach consists of a discrete pose estimator that tracks the most likely sequence of object poses in a coarsely discretized grid, and a continuous pose estimator-controller to refine the pose estimate and accurately manipulate the pose of the grasped object. Our method is tested on diverse objects and configurations, achieving desired manipulation objectives and outperforming single-shot methods in estimation accuracy. The proposed approach holds potential for tasks requiring precise manipulation and limited intrinsic in-hand dexterity under visual occlusion, laying the foundation for closed loop behavior in applications such as regrasping, insertion, and tool use.

“Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects With Video Tracking Enabled Memory Models,” by Yixuan Huang, Jialin Yuan, Chanho Kim, Pupul Pradhan, Bryan Chen, Li Fuxin, and Tucker Hermans from University of Utah, Salt Lake City, Utah, Oregon State University, Corvallis, Ore., and NVIDIA, Seattle, Wash., USA.

Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulation reasoning and planning framework. We propose DOOM and LOOM, which leverage transformer relational dynamics to encode the history of trajectories given partial-view point clouds and an object discovery and tracking engine. Our approaches can perform multiple challenging tasks including reasoning with occluded objects, novel objects appearance, and object reappearance. Throughout our extensive simulation and real world experiments, we find that our approaches perform well in terms of different numbers of objects and different numbers

“Open Sourse Underwater Robot: Easys,” by Michikuni Eguchi, Koki Kato, Tatsuya Oshima, and Shunya Hara from University of Tsukuba and Osaka University, Japan.

“Sensorized Soft Skin for Dexterous Robotic Hands,” by Jana Egli, Benedek Forrai, Thomas Buchner, Jiangtao Su, Xiaodong Chen, and Robert K. Katzschmann from ETH Zurich, Switzerland, and Nanyang Technological University, Singapore.

Conventional industrial robots often use two fingered grippers or suction cups to manipulate objects or interact with the world. Because of their simplified design, they are unable to reproduce the dexterity of human hands when manipulating a wide range of objects. While the control of humanoid hands evolved greatly, hardware platforms still lack capabilities, particularly in tactile sensing and providing soft contact surfaces. In this work, we present a method that equips the skeleton of a tendon-driven humanoid hand with a soft and sensorized tactile skin. Multi-material 3D printing allows us to iteratively approach a cast skin design which preserves the robot’s dexterity in terms of range of motion and speed. We demonstrate that a soft skin enables frmer grasps and piezoresistive sensor integration enhances the hand’s tactile sensing capabilities.


It’s hard to think of a more dramatic way to make an entrance than falling from the sky. While it certainly happens often enough on the silver screen, whether or not it can be done in real life is a tantalizing challenge for our entertainment robotics team at Disney Research.

Falling is tricky for two reasons. The first and most obvious is what Douglas Adams referred to as “the sudden stop at the end.” Every second of free fall means another 9.8 m/s of velocity, and that can quickly add up to an extremely difficult energy dissipation problem. The other tricky thing about falling, especially for terrestrial animals like us, is that our normal methods for controlling our orientation disappear. We are used to relying on contact forces between our body and the environment to control which way we’re pointing. In the air, there’s nothing to push on except the air itself!

Finding a solution to these problems is a big, open-ended challenge. In the clip below, you can see one approach we’ve taken to start chipping away at it.

The video shows a small, stick-like robot with an array of four ducted fans attached to its top. The robot has a piston-like foot that absorbs the impact of a small fall, and then the ducted fans keep the robot standing by counteracting any tilting motion using aerodynamic thrust.

Raphael Pilon [left] and Marcela de los Rios evaluate the performance of the monopod balancing robot.Disney Research

The standing portion demonstrates that pushing on the air isn’t only useful during freefall. Conventional walking and hopping robots depend on ground contact forces to maintain the required orientation. These forces can ramp up quickly because of the stiffness of the system, necessitating high bandwidth control strategies. Aerodynamic forces are relatively soft, but even so, they were sufficient to keep our robots standing. And since these forces can also be applied during the flight phase of running or hopping, this approach might lead to robots that run before they walk. The thing that defines a running gait is the existence of a “flight phase” - a time when none of the feet are in contact with the ground. A running robot with aerodynamic control authority could potentially use a gait with a long flight phase. This would shift the burden of the control effort to mid-flight, simplifying the leg design and possibly making rapid bipedal motion more tractable than a moderate pace.

Richard Landon uses a test rig to evaluate the thrust profile of a ducted fan.Disney Research

In the next video, a slightly larger robot tackles a much more dramatic fall, from 65’ in the air. This simple machine has two piston-like feet and a similar array of ducted fans on top. The fans not only stabilize the robot upon landing, they also help keep it oriented properly as it falls. Inside each foot is a plug of single-use compressible foam. Crushing the foam on impact provides a nice, constant force profile, which maximizes the amount of energy dissipated per inch of contraction.

In the case of this little robot, the mechanical energy dissipation in the pistons is less than the total energy needed to be dissipated from the fall, so the rest of the mechanism takes a pretty hard hit. The size of the robot is an advantage in this case, because scaling laws mean that the strength-to-weight ratio is in its favor.

The strength of a component is a function of its cross-sectional area, while the weight of a component is a function of its volume. Area is proportional to length squared, while volume is proportional to length cubed. This means that as an object gets smaller, its weight becomes relatively small. This is why a toddler can be half the height of an adult but only a fraction of that adult’s weight, and why ants and spiders can run around on long, spindly legs. Our tiny robots take advantage of this, but we can’t stop there if we want to represent some of our bigger characters.

Louis Lambie and Michael Lynch assemble an early ducted fan test platform. The platform was mounted on guidewires and was used for lifting capacity tests.Disney Research

In most aerial robotics applications, control is provided by a system that is capable of supporting the entire weight of the robot. In our case, being able to hover isn’t a necessity. The clip below shows an investigation into how much thrust is needed to control the orientation of a fairly large, heavy robot. The robot is supported on a gimbal, allowing it to spin freely. At the extremities are mounted arrays of ducted fans. The fans don’t have enough force to keep the frame in the air, but they do have a lot of control authority over the orientation.

Complicated robots are less likely to survive unscathed when subjected to the extremely high accelerations of a direct ground impact, as you can see in this early test that didn’t quite go according to plan.

In this last video, we use a combination of the previous techniques and add one more capability – a dramatic mid-air stop. Ducted fans are part of this solution, but the high-speed deceleration is principally accomplished by a large water rocket. Then the mechanical legs only have to handle the last ten feet of dropping acceleration.

Whether it’s using water or rocket fuel, the principle underlying a rocket is the same – mass is ejected from the rocket at high speed, producing a reaction force in the opposite direction via Newton’s third law. The higher the flow rate and the denser the fluid, the more force is produced. To get a high flow rate and a quick response time, we needed a wide nozzle that went from closed to open cleanly in a matter of milliseconds. We designed a system using a piece of copper foil and a custom punch mechanism that accomplished just that.

Grant Imahara pressurizes a test tank to evaluate an early valve prototype [left]. The water rocket in action - note the laminar, two-inch-wide flow as it passes through the specially designed nozzleDisney Research

Once the water rocket has brought the robot to a mid-air stop, the ducted fans are able to hold it in a stable hover about ten feet above the deck. When they cut out, the robot falls again and the legs absorb the impact. In the video, the robot has a couple of loose tethers attached as a testing precaution, but they don’t provide any support, power, or guidance.

“It might not be so obvious as to what this can be directly used for today, but these rough proof-of-concept experiments show that we might be able to work within real-world physics to do the high falls our characters do on the big screen, and someday actually stick the landing,” explains Tony Dohi, the project lead.

There are still a large number of problems for future projects to address. Most characters have legs that bend on hinges rather than compress like pistons, and don’t wear a belt made of ducted fans. Beyond issues of packaging and form, making sure that the robot lands exactly where it intends to land has interesting implications for perception and control. Regardless, we think we can confirm that this kind of entrance has–if you’ll excuse the pun–quite the impact.



Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

ICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDSICSR 2024: 23–26 October 2024, ODENSE, DENMARKCybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

Festo has robot bees!

It’s a very clever design, but the size makes me terrified of whatever the bees are that Festo seems to be familiar with.

[ Festo ]

Boing, boing, boing!

[ USC ]

Why the heck would you take the trouble to program a robot to make sweet potato chips and then not scarf them down yourself?

[ Dino Robotics ]

Mobile robots can transport payloads far greater than their mass through vehicle traction. However, off-road terrain features substantial variation in height, grade, and friction, which can cause traction to degrade or fail catastrophically. This paper presents a system that utilizes a vehicle-mounted, multipurpose manipulator to physically adapt the robot with unique anchors suitable for a particular terrain for autonomous payload transport.

[ DART Lab ]

Turns out that working on a collaborative task with a robot can make humans less efficient, because we tend to overestimate the robot’s capabilities.

[ CHI 2024 ]

Wing posts a video with the title “What Do Wing’s Drones Sound Like” but only includes a brief snippet—though nothing without background room noise—revealing to curious viewers and listeners exactly what Wing’s drones sound like.

Because, look, a couple seconds of muted audio underneath a voiceover is in fact not really answering the question.

[ Wing ]

This first instance of ROB 450 in Winter 2024 challenged students to synthesize the knowledge acquired through their Robotics undergraduate courses at the University of Michigan to use a systematic and iterative design and analysis process and apply it to solving a real, open-ended Robotics problem.

[ Michigan Robotics ]

This Microsoft Future Leaders in Robotics and AI Seminar is from Catie Cuan at Stanford, on “Choreorobotics: Teaching Robots How to Dance With Humans.”

As robots transition from industrial and research settings into everyday environments, robots must be able to (1) learn from humans while benefiting from the full range of the humans’ knowledge and (2) learn to interact with humans in safe, intuitive, and social ways. I will present a series of compelling robot behaviors, where human perception and interaction are foregrounded in a variety of tasks.

[ UMD ]



For years, Shadow Robot Company’s Shadow Hand has arguably been the gold standard for robotic manipulation. Beautiful and expensive, it is able to mimic the form factor and functionality of human hands, which has made it ideal for complex tasks. I’ve personally experienced how amazing it is to use Shadow Hands in a teleoperation context, and it’s hard to imagine anything better.

The problem with the original Shadow hand was (and still is) fragility. In a research environment, this has been fine, except that research is changing: Roboticists no longer carefully program manipulation tasks by, uh, hand. Now it’s all about machine learning, in which you need robotic hands to massively fail over and over again until they build up enough data to understand how to succeed.

“We’ve aimed for robustness and performance over anthropomorphism and human size and shape.” —Rich Walker, Shadow Robot Company

Doing this with a Shadow Hand was just not realistic, which Google DeepMind understood five years ago when it asked Shadow Robot to build it a new hand with hardware that could handle the kind of training environments that now typify manipulation research. So Shadow Robot spent the last half-decade-ish working on a new, three-fingered Shadow Hand, which the company unveiled today. The company is calling it, appropriately enough, “the new Shadow Hand.”

As you can see, this thing is an absolute beast. Shadow Robot says that the new hand is “robust against a significant amount of misuse, including aggressive force demands, abrasion and impacts.” Part of the point, though, is that what robot-hand designers might call “misuse,” robot-manipulation researchers might very well call “progress,” and the hand is designed to stand up to manipulation research that pushes the envelope of what robotic hardware and software are physically capable of.

Shadow Robot understands that despite its best engineering efforts, this new hand will still occasionally break (because it’s a robot and that’s what robots do), so the company designed it to be modular and easy to repair. Each finger is its own self-contained unit that can be easily swapped out, with five Maxon motors in the base of the finger driving the four finger joints through cables in a design that eliminates backlash. The cables themselves will need replacement from time to time, but it’s much easier to do this on the new Shadow Hand than it was on the original. Shadow Robot says that you can swap out an entire New Hand’s worth of cables in the same time it would take you to replace a single cable on the old hand.

Shadow Robot

The new Shadow Hand itself is somewhat larger than a typical human hand, and heavier too: Each modular finger unit weighs 1.2 kilograms, and the entire three-fingered hand is just over 4 kg. The fingers have humanlike kinematics, and each joint can move up to 180 degrees per second with the capability of exerting at least 8 newtons of force at each fingertip. Both force control and position control are available, and the entire hand runs Robot Operating System, the Open Source Robotics Foundation’s collection of open-source software libraries and tools.

One of the coolest new features of this hand is the tactile sensing. Shadow Robot has decided to take the optical route with fingertip sensors, GelSight-style. Each fingertip is covered in soft, squishy gel with thousands of embedded particles. Cameras in the fingers behind the gel track each of those particles, and when the fingertip touches something, the particles move. Based on that movement, the fingertips can very accurately detect the magnitude and direction of even very small forces. And there are even more sensors on the insides of the fingers too, with embedded Hall effect sensors to help provide feedback during grasping and manipulation tasks.

Shadow Robot

The most striking difference here is how completely different of a robotic-manipulation philosophy this new hand represents for Shadow Robot. “We’ve aimed for robustness and performance over anthropomorphism and human size and shape,” says Rich Walker, director of Shadow Robot Company. “There’s a very definite design choice there to get something that really behaves much more like an optimized manipulator rather than a humanlike hand.”

Walker explains that Shadow Robot sees two different approaches to manipulation within the robotics community right now: There’s imitation learning, where a human does a task and then a robot tries to do the task the same way, and then there’s reinforcement learning, where a robot tries to figure out how do the task by itself. “Obviously, this hand was built from the ground up to make reinforcement learning easy.”

The hand was also built from the ground up to be rugged and repairable, which had a significant effect on the form factor. To make the fingers modular, they have to be chunky, and trying to cram five of them onto one hand was just not practical. But because of this modularity, Shadow Robot could make you a five-fingered hand if you really wanted one. Or a two-fingered hand. Or (and this is the company’s suggestion, not mine) “a giant spider.” Really, though, it’s probably not useful to get stuck on the form factor. Instead, focus more on what the hand can do. In fact, Shadow Robot tells me that the best way to think about the hand in the context of agility is as having three thumbs, not three fingers, but Walker says that “if we describe it as that, people get confused.”

There’s still definitely a place for the original anthropomorphic Shadow Hand, and Shadow Robot has no plans to discontinue it. “It’s clear that for some people anthropomorphism is a deal breaker, they have to have it,” Walker says. “But for a lot of people, the idea that they could have something which is really robust and dexterous and can gather lots of data, that’s exciting enough to be worth saying okay, what can we do with this? We’re very interested to find out what happens.”

The Shadow New Hand is available now, starting at about US $74,000 depending on configuration.



Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Eurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCEICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDSCybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

In this work, we present LocoMan, a dexterous quadrupedal robot with a novel morphology to perform versatile manipulation in diverse constrained environments. By equipping a Unitree Go1 robot with two low-cost and lightweight modular 3-DoF loco-manipulators on its front calves, LocoMan leverages the combined mobility and functionality of the legs and grippers for complex manipulation tasks that require precise 6D positioning of the end effector in a wide workspace.

[ CMU ]

Thanks, Changyi!

Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. In this paper, we present an optimization approach to solving the loco-manipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA.

[ Silicon Synapse Lab ]

Okay, but where that costume has eyes is not where Spot has eyes, so the Spot in the costume can’t see, right? And now I’m skeptical of the authenticity of the mutual snoot-boop.

[ Boston Dynamics ]

Here’s some video of Field AI’s robots operating in relatively complex and unstructured environments without prior maps. Make sure to read our article from this week for details!

[ Field AI ]

Is it just me, or is it kind of wild that researchers are now publishing papers comparing their humanoid controller to the “manufacturer’s” humanoid controller? It’s like humanoids are a commodity now or something.

[ OSU ]

I, too, am packing armor for ICRA.

[ Pollen Robotics ]

Honey Badger 4.0 is our latest robotic platform, created specifically for traversing hostile environments and difficult terrains. Equipped with multiple cameras and sensors, it will make sure no defect is omitted during inspection.

[ MAB Robotics ]

Thanks, Jakub!

Have an automation task that calls for the precision and torque of an industrial robot arm…but you need something that is more rugged or a non-conventional form factor? Meet the HEBI Robotics H-Series Actuator! With 9x the torque of our X-Series and seamless compatibility with the HEBI ecosystem for robot development, the H-Series opens a new world of possibilities for robots.

[ HEBI ]

Thanks, Dave!

This is how all spills happen at my house too: super passive-aggressively.

[ 1X ]

EPFL’s team led by PhD student Milad Shafiee, along with co-authors Guillaume Bellegarda and BioRobotics Lab head Auke Ijspeert, have trained a four-legged robot using deep reinforcement learning to navigate challenging terrain, achieving a milestone in both robotics and biology.

[ EPFL ]

At Agility, we make robots that are made for work. Our robot Digit works alongside us in spaces designed for people. Digit handles the tedious and repetitive tasks meant for a machine, allowing companies and their people to focus on the work that requires the human element.

[ Agility ]

With a wealth of incredible figures and outstanding facts, here’s Jan Jonsson, ABB Robotics veteran, sharing his knowledge and passion for some of our robots and controllers from the past.

[ ABB ]

I have it on good authority that getting robots to mow a lawn (like, any lawn) is much harder than it looks, but Electric Sheep has built a business around it.

[ Electric Sheep ]

The AI Index, currently in its seventh year, tracks, collates, distills, and visualizes data relating to artificial intelligence. The Index provides unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, journalists, executives, and the general public to develop a deeper understanding of the complex field of AI. Led by a steering committee of influential AI thought leaders, the Index is the world’s most comprehensive report on trends in AI. In this seminar, HAI Research Manager Nestor Maslej offers highlights from the 2024 report, explaining trends related to research and development, technical performance, technical AI ethics, the economy, education, policy and governance, diversity, and public opinion.

[ Stanford HAI ]

This week’s CMU RI Seminar is from Dieter Fox at NVIDIA and UW, on “Where’s RobotGPT?”

In this talk, I will discuss approaches to generating large datasets for training robot manipulation capabilities, with a focus on the role simulation can play in this context. I will show some of our prior work, where we demonstrated robust sim-to-real transfer of manipulation skills trained in simulation, and then present a path toward generating large scale demonstration sets that could help train robust, open-world robot manipulation models.

[ CMU ]



One of the biggest challenges for robotics right now is practical autonomous operation in unstructured environments. That is, doing useful stuff in places your robot hasn’t been before and where things may not be as familiar as your robot might like. Robots thrive on predictability, which has put some irksome restrictions on where and how they can be successfully deployed.

But over the past few years, this has started to change, thanks in large part to a couple of pivotal robotics challenges put on by DARPA. The DARPA Subterranean Challenge ran from 2018 to 2021, putting mobile robots through a series of unstructured underground environments. And the currently ongoing DARPA RACER program tasks autonomous vehicles with navigating long distances off-road. Some extremely impressive technology has been developed through these programs, but there’s always a gap between this cutting-edge research and any real-world applications.

Now, a bunch of the folks involved in these challenges, including experienced roboticists from NASA, DARPA, Google DeepMind, Amazon, and Cruise (to name just a few places) are applying everything that they’ve learned to enable real-world practical autonomy for mobile robots at a startup called Field AI.

Field AI was cofounded by Ali Agha, who previously was the leader of NASA JPL’s Aerial Mobility Group. While at JPL, Agha led Team CoSTAR, which won the DARPA Subterranean Challenge Urban Circuit. Agha has also been the principal investigator for DARPA RACER, first with JPL, and now continuing with Field AI. “Field AI is not just a startup,” Agha tells us. “It’s a culmination of decades of experience in AI and its deployment in the field.”

Unstructured environments are where things are constantly changing, which can play havoc with robots that rely on static maps.

The “field” part in Field AI is what makes Agha’s startup unique. Robots running Field AI’s software are able to handle unstructured, unmapped environments without reliance on prior models, GPS, or human intervention. Obviously, this kind of capability was (and is) of interest to NASA and JPL, which send robots to places where there are no maps, GPS doesn’t exist, and direct human intervention is impossible.

But DARPA SubT demonstrated that similar environments can be found on Earth, too. For instance, mines, natural caves, and the urban underground are all extremely challenging for robots (and even for humans) to navigate. And those are just the most extreme examples: robots that need to operate inside buildings or out in the wilderness have similar challenges understanding where they are, where they’re going, and how to navigate the environment around them.

An autonomous vehicle drives across kilometers of desert with no prior map, no GPS, and no road.Field AI

Despite the difficulty that robots have operating in the field, this is an enormous opportunity that Field AI hopes to address. Robots have already proven their worth in inspection contexts, typically where you either need to make sure that nothing is going wrong across a large industrial site, or for tracking construction progress inside a partially completed building. There’s a lot of value here because the consequences of something getting messed up are expensive or dangerous or both, but the tasks are repetitive and sometimes risky and generally don’t require all that much human insight or creativity.

Uncharted Territory as Home Base

Where Field AI differs from other robotics companies offering these services, as Agha explains, is that his company wants to do these tasks without first having a map that tells the robot where to go. In other words, there’s no lengthy setup process, and no human supervision, and the robot can adapt to changing and new environments. Really, this is what full autonomy is all about: going anywhere, anytime, without human interaction. “Our customers don’t need to train anything,” Agha says, laying out the company’s vision. “They don’t need to have precise maps. They press a single button, and the robot just discovers every corner of the environment.” This capability is where the DARPA SubT heritage comes in. During the competition, DARPA basically said, “here’s the door into the course. We’re not going to tell you anything about what’s back there or even how big it is. Just go explore the whole thing and bring us back the info we’ve asked for.” Agha’s Team CoSTAR did exactly that during the competition, and Field AI is commercializing this capability.

“With our robots, our aim is for you to just deploy it, with no training time needed. And then we can just leave the robots.” —Ali Agha, Field AI

The other tricky thing about these unstructured environments, especially construction environments, is that things are constantly changing, which can play havoc with robots that rely on static maps. “We’re one of the few, if not the only company that can leave robots for days on continuously changing construction sites with minimal supervision,” Agha tells us. “These sites are very complex—every day there are new items, new challenges, and unexpected events. Construction materials on the ground, scaffolds, forklifts, and heavy machinery moving all over the place, nothing you can predict.”

Field AI

Field AI’s approach to this problem is to emphasize environmental understanding over mapping. Agha says that essentially, Field AI is working towards creating “field foundation models” (FFMs) of the physical world, using sensor data as an input. You can think of FFMs as being similar to the foundation models of language, music, and art that other AI companies have created over the past several years, where ingesting a large amount of data from the Internet enables some level of functionality in a domain without requiring specific training for each new situation. Consequently, Field AI’s robots can understand how to move in the world, rather than just where to move. “We look at AI quite differently from what’s mainstream,” Agha explains. “We do very heavy probabilistic modeling.” Much more technical detail would get into Field AI’s IP, says Agha, but the point is that real-time world modeling becomes a by-product of Field AI’s robots operating in the world rather than a prerequisite for that operation. This makes the robots fast, efficient, and resilient.

Developing field-foundation models that robots can use to reliably go almost anywhere requires a lot of real-world data, which Field AI has been collecting at industrial and construction sites around the world for the past year. To be clear, they’re collecting the data as part of their commercial operations—these are paying customers that Field AI has already. “In these job sites, it can traditionally take weeks to go around a site and map where every single target of interest that you need to inspect is,” explains Agha. “But with our robots, our aim is for you to just deploy it, with no training time needed. And then we can just leave the robots. This level of autonomy really unlocks a lot of use cases that our customers weren’t even considering, because they thought it was years away.” And the use cases aren’t just about construction or inspection or other areas where we’re already seeing autonomous robotic systems, Agha says. “These technologies hold immense potential.”

There’s obviously demand for this level of autonomy, but Agha says that the other piece of the puzzle that will enable Field AI to leverage a trillion dollar market is the fact that they can do what they do with virtually any platform. Fundamentally, Field AI is a software company—they make sensor payloads that integrate with their autonomy software, but even those payloads are adjustable, ranging from something appropriate for an autonomous vehicle to something that a drone can handle.

Heck, if you decide that you need an autonomous humanoid for some weird reason, Field AI can do that too. While the versatility here is important, according to Agha, what’s even more important is that it means you can focus on platforms that are more affordable, and still expect the same level of autonomous performance, within the constraints of each robot’s design, of course. With control over the full software stack, integrating mobility with high-level planning, decision making, and mission execution, Agha says that the potential to take advantage of relatively inexpensive robots is what’s going to make the biggest difference toward Field AI’s commercial success.

Same brain, lots of different robots: the Field AI team’s foundation models can be used on robots big, small, expensive, and somewhat less expensive.Field AI

Field AI is already expanding its capabilities, building on some of its recent experience with DARPA RACER by working on deploying robots to inspect pipelines for tens of kilometers and to transport materials across solar farms. With revenue coming in and a substantial chunk of funding, Field AI has even attracted interest from Bill Gates. Field AI’s participation in RACER is ongoing, under a sort of subsidiary company for federal projects called Offroad Autonomy, and in the meantime its commercial side is targeting expansion to “hundreds” of sites on every platform it can think of, including humanoids.



Editor’s note: This article is adapted from the author’s book War Virtually: The Quest to Automate Conflict, Militarize Data, and Predict the Future (University of California Press, published in paperback April 2024).

The blistering late-afternoon wind ripped across Camp Taji, a sprawling U.S. military base just north of Baghdad. In a desolate corner of the outpost, where the feared Iraqi Republican Guard had once manufactured mustard gas, nerve agents, and other chemical weapons, a group of American soldiers and Marines were solemnly gathered around an open grave, dripping sweat in the 114-degree heat. They were paying their final respects to Boomer, a fallen comrade who had been an indispensable part of their team for years. Just days earlier, he had been blown apart by a roadside bomb.

As a bugle mournfully sounded the last few notes of “Taps,” a soldier raised his rifle and fired a long series of volleys—a 21-gun salute. The troops, which included members of an elite army unit specializing in explosive ordnance disposal (EOD), had decorated Boomer posthumously with a Bronze Star and a Purple Heart. With the help of human operators, the diminutive remote-controlled robot had protected American military personnel from harm by finding and disarming hidden explosives.

Boomer was a Multi-function Agile Remote-Controlled robot, or MARCbot, manufactured by a Silicon Valley company called Exponent. Weighing in at just over 30 pounds, MARCbots look like a cross between a Hollywood camera dolly and an oversized Tonka truck. Despite their toylike appearance, the devices often leave a lasting impression on those who work with them. In an online discussion about EOD support robots, one soldier wrote, “Those little bastards can develop a personality, and they save so many lives.” An infantryman responded by admitting, “We liked those EOD robots. I can’t blame you for giving your guy a proper burial, he helped keep a lot of people safe and did a job that most people wouldn’t want to do.”

A Navy unit used a remote-controlled vehicle with a mounted video camera in 2009 to investigate suspicious areas in southern Afghanistan.Mass Communication Specialist 2nd Class Patrick W. Mullen III/U.S. Navy

But while some EOD teams established warm emotional bonds with their robots, others loathed the machines, especially when they malfunctioned. Take, for example, this case described by a Marine who served in Iraq:

My team once had a robot that was obnoxious. It would frequently accelerate for no reason, steer whichever way it wanted, stop, etc. This often resulted in this stupid thing driving itself into a ditch right next to a suspected IED. So of course then we had to call EOD [personnel] out and waste their time and ours all because of this stupid little robot. Every time it beached itself next to a bomb, which was at least two or three times a week, we had to do this. Then one day we saw yet another IED. We drove him straight over the pressure plate, and blew the stupid little sh*thead of a robot to pieces. All in all a good day.

Some battle-hardened warriors treat remote-controlled devices like brave, loyal, intelligent pets, while others describe them as clumsy, stubborn clods. Either way, observers have interpreted these accounts as unsettling glimpses of a future in which men and women ascribe personalities to artificially intelligent war machines.

Some battle-hardened warriors treat remote-controlled devices like brave, loyal, intelligent pets, while others describe them as clumsy, stubborn clods.

From this perspective, what makes robot funerals unnerving is the idea of an emotional slippery slope. If soldiers are bonding with clunky pieces of remote-controlled hardware, what are the prospects of humans forming emotional attachments with machines once they’re more autonomous in nature, nuanced in behavior, and anthropoid in form? And a more troubling question arises: On the battlefield, will Homo sapiens be capable of dehumanizing members of its own species (as it has for centuries), even as it simultaneously humanizes the robots sent to kill them?

As I’ll explain, the Pentagon has a vision of a warfighting force in which humans and robots work together in tight collaborative units. But to achieve that vision, it has called in reinforcements: “trust engineers” who are diligently helping the Department of Defense (DOD) find ways of rewiring human attitudes toward machines. You could say that they want more soldiers to play “Taps” for their robot helpers and fewer to delight in blowing them up.

The Pentagon’s Push for Robotics

For the better part of a decade, several influential Pentagon officials have relentlessly promoted robotic technologies, promising a future in which “humans will form integrated teams with nearly fully autonomous unmanned systems, capable of carrying out operations in contested environments.”

Soldiers test a vertical take-off-and-landing drone at Fort Campbell, Ky., in 2020.U.S. Army

As The New York Times reported in 2016: “Almost unnoticed outside defense circles, the Pentagon has put artificial intelligence at the center of its strategy to maintain the United States’ position as the world’s dominant military power.” The U.S. government is spending staggering sums to advance these technologies: For fiscal year 2019, the U.S. Congress was projected to provide the DOD with US $9.6 billion to fund uncrewed and robotic systems—significantly more than the annual budget of the entire National Science Foundation.

Arguments supporting the expansion of autonomous systems are consistent and predictable: The machines will keep our troops safe because they can perform dull, dirty, dangerous tasks; they will result in fewer civilian casualties, since robots will be able to identify enemies with greater precision than humans can; they will be cost-effective and efficient, allowing more to get done with less; and the devices will allow us to stay ahead of China, which, according to some experts, will soon surpass America’s technological capabilities.

Former U.S. deputy defense secretary Robert O. Work has argued for more automation within the military. Center for a New American Security

Among the most outspoken advocate of a roboticized military is Robert O. Work, who was nominated by President Barack Obama in 2014 to serve as deputy defense secretary. Speaking at a 2015 defense forum, Work—a barrel-chested retired Marine Corps colonel with the slight hint of a drawl—described a future in which “human-machine collaboration” would win wars using big-data analytics. He used the example of Lockheed Martin’s newest stealth fighter to illustrate his point: “The F-35 is not a fighter plane, it is a flying sensor computer that sucks in an enormous amount of data, correlates it, analyzes it, and displays it to the pilot on his helmet.”

The beginning of Work’s speech was measured and technical, but by the end it was full of swagger. To drive home his point, he described a ground combat scenario. “I’m telling you right now,” Work told the rapt audience, “10 years from now if the first person through a breach isn’t a friggin’ robot, shame on us.”

“The debate within the military is no longer about whether to build autonomous weapons but how much independence to give them,” said a 2016 New York Times article. The rhetoric surrounding robotic and autonomous weapon systems is remarkably similar to that of Silicon Valley, where charismatic CEOs, technology gurus, and sycophantic pundits have relentlessly hyped artificial intelligence.

For example, in 2016, the Defense Science Board—a group of appointed civilian scientists tasked with giving advice to the DOD on technical matters—released a report titled “Summer Study on Autonomy.” Significantly, the report wasn’t written to weigh the pros and cons of autonomous battlefield technologies; instead, the group assumed that such systems will inevitably be deployed. Among other things, the report included “focused recommendations to improve the future adoption and use of autonomous systems [and] example projects intended to demonstrate the range of benefits of autonomy for the warfighter.”

What Exactly Is a Robot Soldier?

The author’s book, War Virtually, is a critical look at how the U.S. military is weaponizing technology and data.University of California Press

Early in the 20th century, military and intelligence agencies began developing robotic systems, which were mostly devices remotely operated by human controllers. But microchips, portable computers, the Internet, smartphones, and other developments have supercharged the pace of innovation. So, too, has the ready availability of colossal amounts of data from electronic sources and sensors of all kinds. The Financial Times reports: “The advance of artificial intelligence brings with it the prospect of robot-soldiers battling alongside humans—and one day eclipsing them altogether.” These transformations aren’t inevitable, but they may become a self-fulfilling prophecy.

All of this raises the question: What exactly is a “robot-soldier”? Is it a remote-controlled, armor-clad box on wheels, entirely reliant on explicit, continuous human commands for direction? Is it a device that can be activated and left to operate semiautonomously, with a limited degree of human oversight or intervention? Is it a droid capable of selecting targets (using facial-recognition software or other forms of artificial intelligence) and initiating attacks without human involvement? There are hundreds, if not thousands, of possible technological configurations lying between remote control and full autonomy—and these differences affect ideas about who bears responsibility for a robot’s actions.

The U.S. military’s experimental and actual robotic and autonomous systems include a vast array of artifacts that rely on either remote control or artificial intelligence: aerial drones; ground vehicles of all kinds; sleek warships and submarines; automated missiles; and robots of various shapes and sizes—bipedal androids, quadrupedal gadgets that trot like dogs or mules, insectile swarming machines, and streamlined aquatic devices resembling fish, mollusks, or crustaceans, to name a few.

Members of a U.S. Air Force squadron test out an agile and rugged quadruped robot from Ghost Robotics in 2023.Airman First Class Isaiah Pedrazzini/U.S. Air Force

The transitions projected by military planners suggest that servicemen and servicewomen are in the midst of a three-phase evolutionary process, which begins with remote-controlled robots, in which humans are “in the loop,” then proceeds to semiautonomous and supervised autonomous systems, in which humans are “on the loop,” and then concludes with the adoption of fully autonomous systems, in which humans are “out of the loop.” At the moment, much of the debate in military circles has to do with the degree to which automated systems should allow—or require—human intervention.

“Ten years from now if the first person through a breach isn’t a friggin’ robot, shame on us.” —Robert O. Work

In recent years, much of the hype has centered around that second stage: semiautonomous and supervised autonomous systems that DOD officials refer to as “human-machine teaming.” This idea suddenly appeared in Pentagon publications and official statements after the summer of 2015. The timing probably wasn’t accidental; it came at a time when global news outlets were focusing attention on a public backlash against lethal autonomous weapon systems. The Campaign to Stop Killer Robots was launched in April 2013 as a coalition of nonprofit and civil society organizations, including the International Committee for Robot Arms Control, Amnesty International, and Human Rights Watch. In July 2015, the campaign released an open letter warning of a robotic arms race and calling for a ban on the technologies. Cosigners included world-renowned physicist Stephen Hawking, Tesla founder Elon Musk, Apple cofounder Steve Wozniak, and thousands more.

In November 2015, Work gave a high-profile speech on the importance of human-machine teaming, perhaps hoping to defuse the growing criticism of “killer robots.” According to one account, Work’s vision was one in which “computers will fly the missiles, aim the lasers, jam the signals, read the sensors, and pull all the data together over a network, putting it into an intuitive interface humans can read, understand, and use to command the mission”—but humans would still be in the mix, “using the machine to make the human make better decisions.” From this point forward, the military branches accelerated their drive toward human-machine teaming.

The Doubt in the Machine

But there was a problem. Military experts loved the idea, touting it as a win-win: Paul Scharre, in his book Army of None: Autonomous Weapons and the Future of War, claimed that “we don’t need to give up the benefits of human judgment to get the advantages of automation, we can have our cake and eat it too.” However, personnel on the ground expressed—and continue to express—deep misgivings about the side effects of the Pentagon’s newest war machines.

The difficulty, it seems, is humans’ lack of trust. The engineering challenges of creating robotic weapon systems are relatively straightforward, but the social and psychological challenges of convincing humans to place their faith in the machines are bewilderingly complex. In high-stakes, high-pressure situations like military combat, human confidence in autonomous systems can quickly vanish. The Pentagon’s Defense Systems Information Analysis Center Journal noted that although the prospects for combined human-machine teams are promising, humans will need assurances:

[T]he battlefield is fluid, dynamic, and dangerous. As a result, warfighter demands become exceedingly complex, especially since the potential costs of failure are unacceptable. The prospect of lethal autonomy adds even greater complexity to the problem [in that] warfighters will have no prior experience with similar systems. Developers will be forced to build trust almost from scratch.

In a 2015 article, U.S. Navy Commander Greg Smith provided a candid assessment of aviators’ distrust in aerial drones. After describing how drones are often intentionally separated from crewed aircraft, Smith noted that operators sometimes lose communication with their drones and may inadvertently bring them perilously close to crewed airplanes, which “raises the hair on the back of an aviator’s neck.” He concluded:

[I]n 2010, one task force commander grounded his manned aircraft at a remote operating location until he was assured that the local control tower and UAV [unmanned aerial vehicle] operators located halfway around the world would improve procedural compliance. Anecdotes like these abound…. After nearly a decade of sharing the skies with UAVs, most naval aviators no longer believe that UAVs are trying to kill them, but one should not confuse this sentiment with trusting the platform, technology, or [drone] operators.

U.S. Marines [top] prepare to launch and operate a MQ-9A Reaper drone in 2021. The Reaper [bottom] is designed for both high-altitude surveillance and destroying targets.Top: Lance Cpl. Gabrielle Sanders/U.S. Marine Corps; Bottom: 1st Lt. John Coppola/U.S. Marine Corps

Yet Pentagon leaders place an almost superstitious trust in those systems, and seem firmly convinced that a lack of human confidence in autonomous systems can be overcome with engineered solutions. In a commentary, Courtney Soboleski, a data scientist employed by the military contractor Booz Allen Hamilton, makes the case for mobilizing social science as a tool for overcoming soldiers’ lack of trust in robotic systems.

The problem with adding a machine into military teaming arrangements is not doctrinal or numeric…it is psychological. It is rethinking the instinctual threshold required for trust to exist between the soldier and machine.… The real hurdle lies in surpassing the individual psychological and sociological barriers to assumption of risk presented by algorithmic warfare. To do so requires a rewiring of military culture across several mental and emotional domains.… AI [artificial intelligence] trainers should partner with traditional military subject matter experts to develop the psychological feelings of safety not inherently tangible in new technology. Through this exchange, soldiers will develop the same instinctual trust natural to the human-human war-fighting paradigm with machines. The Military’s Trust Engineers Go to Work

Soon, the wary warfighter will likely be subjected to new forms of training that focus on building trust between robots and humans. Already, robots are being programmed to communicate in more human ways with their users for the explicit purpose of increasing trust. And projects are currently underway to help military robots report their deficiencies to humans in given situations, and to alter their functionality according to the machine’s perceived emotional state of the user.

At the DEVCOM Army Research Laboratory, military psychologists have spent more than a decade on human experiments related to trust in machines. Among the most prolific is Jessie Chen, who joined the lab in 2003. Chen lives and breathes robotics—specifically “agent teaming” research, a field that examines how robots can be integrated into groups with humans. Her experiments test how humans’ lack of trust in robotic and autonomous systems can be overcome—or at least minimized.

For example, in one set of tests, Chen and her colleagues deployed a small ground robot called an Autonomous Squad Member that interacted and communicated with infantrymen. The researchers varied “situation-awareness-based agent transparency”—that is, the robot’s self-reported information about its plans, motivations, and predicted outcomes—and found that human trust in the robot increased when the autonomous “agent” was more transparent or honest about its intentions.

The Army isn’t the only branch of the armed services researching human trust in robots. The U.S. Air Force Research Laboratory recently had an entire group dedicated to the subject: the Human Trust and Interaction Branch, part of the lab’s 711th Human Performance Wing, located at Wright-Patterson Air Force Base, in Ohio.

In 2015, the Air Force began soliciting proposals for “research on how to harness the socio-emotional elements of interpersonal team/trust dynamics and inject them into human-robot teams.” Mark Draper, a principal engineering research psychologist at the Air Force lab, is optimistic about the prospects of human-machine teaming: “As autonomy becomes more trusted, as it becomes more capable, then the Airmen can start off-loading more decision-making capability on the autonomy, and autonomy can exercise increasingly important levels of decision-making.”

Air Force researchers are attempting to dissect the determinants of human trust. In one project, they examined the relationship between a person’s personality profile (measured using the so-called Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, neuroticism) and his or her tendency to trust. In another experiment, entitled “Trusting Robocop: Gender-Based Effects on Trust of an Autonomous Robot,” Air Force scientists compared male and female research subjects’ levels of trust by showing them a video depicting a guard robot. The robot was armed with a Taser, interacted with people, and eventually used the Taser on one. Researchers designed the scenario to create uncertainty about whether the robot or the humans were to blame. By surveying research subjects, the scientists found that women reported higher levels of trust in “Robocop” than men.

The issue of trust in autonomous systems has even led the Air Force’s chief scientist to suggest ideas for increasing human confidence in the machines, ranging from better android manners to robots that look more like people, under the principle that

good HFE [human factors engineering] design should help support ease of interaction between humans and AS [autonomous systems]. For example, better “etiquette” often equates to better performance, causing a more seamless interaction. This occurs, for example, when an AS avoids interrupting its human teammate during a high workload situation or cues the human that it is about to interrupt—activities that, surprisingly, can improve performance independent of the actual reliability of the system. To an extent, anthropomorphism can also improve human-AS interaction, since people often trust agents endowed with more humanlike features…[but] anthropomorphism can also induce overtrust.

It’s impossible to know the degree to which the trust engineers will succeed in achieving their objectives. For decades, military trainers have trained and prepared newly enlisted men and women to kill other people. If specialists have developed simple psychological techniques to overcome the soldier’s deeply ingrained aversion to destroying human life, is it possible that someday, the warfighter might also be persuaded to unquestioningly place his or her trust in robots?



Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Eurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCEICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDSCybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program recently conducted its fourth experiment (E4) to assess the performance of off-road unmanned vehicles. These tests, conducted in Texas in late 2023, were the first time the program tested its new vehicle, the RACER Heavy Platform (RHP). The video shows autonomous route following for mobility testing and demonstration, including sensor point cloud visualizations.

The 12-ton RHP is significantly larger than the 2-ton RACER Fleet Vehicles (RFVs) already in use in the program. Using the algorithms on a very different platform helps RACER toward its goal of platform agnostic autonomy of combat-scale vehicles in complex, mission-relevant off-road environments that are significantly more unpredictable than on-road conditions.

[ DARPA ]

In our new Science Robotics paper, we introduce an autonomous navigation system developed for our wheeled-legged quadrupeds, designed for fast and efficient navigation within large urban environments. Driven by neural network policies, our simple, unified control system enables smooth gait transitions, smart navigation planning, and highly responsive obstacle avoidance in populated urban environments.

[ Github ]

Generation 7 of “Phoenix” robots include improved human-like range of motion. Improvements in uptime, visual perception, and tactile sensing increase the capability of the system to perform complex tasks over longer periods. Design iteration significantly decreases build time. The speed at which new tasks can be automated has increased 50x, marking a major inflection point in task automation speed.

[ Sanctuary AI ]

We’re proud to celebrate our one millionth commercial delivery—that’s a million deliveries of lifesaving blood, critical vaccines, last-minute groceries, and so much more. But the best part? This is just the beginning.

[ Zipline ]

Work those hips!

[ RoMeLa ]

This thing is kind of terrifying, and I’m fascinated by it.

[ AVFL ]

We propose a novel humanoid TWIMP, which combines a human mimetic musculoskeletal upper limb with a two-wheel inverted pendulum. By combining the benefit of a musculoskeletal humanoid, which can achieve soft contact with the external environment, and the benefit of a two-wheel inverted pendulum with a small footprint and high mobility, we can easily investigate learning control systems in environments with contact and sudden impact.

From Humanoids 2018.

[ Paper ] via [ JSK Lab ]

Thanks, Kento!

Ballbots are uniquely capable of pushing wheelchairs—arguably better than legged platforms, because they can move in any direction without having to reposition themselves.

[ Paper ]

Charge Robotics is building robots that automate the most labor-intensive parts of solar construction. Solar has rapidly become the cheapest form of power generation in many regions. Demand has skyrocketed, and now the primary barrier to getting it installed is labor logistics and bandwidth. Our robots remove the labor bottleneck, allowing construction companies to meet the rising demand for solar, and enabling the world to switch to renewables faster.

[ Charge Robotics ]

Robots doing precision assembly is cool and all, but those vibratory bowl sorters seem like magic.

[ FANUC ]

The QUT CGRAS project’s robot prototype captures images of baby corals, destined for the Great Barrier Reef, monitoring and counting them in grow tanks. The team uses state-of-the-art AI algorithms to automatically detect and count these coral babies and track their growth over time – saving human counting time and money.

[ QUT ]

We are conducting research to develop Unmanned Aerial Systems to aid in wildfire monitoring. The hazardous, dynamic, and visually degraded environment of wildfire gives rise to many unsolved fundamental research challenges.

[ CMU ]

Here’s a little more video of that robot elevator, but I’m wondering why it’s so slow—clamp those bots in there and rocket that elevator up and down!

[ NAVER ]

In March 2024, Northwestern University’s Center for Robotics and Biosystems demonstrated the Omnid mobile collaborative robots (mocobots) at MARS, a conference in Ojai, California on Machine learning, Automation, Robotics, and Space, hosted by Jeff Bezos. The “swarm” of mocobots is designed to collaborate with humans, allowing a human to easily manipulate large, heavy, or awkward payloads. In this case, the mocobots cancel the effect of gravity, so the human can easily manipulate the mock airplane wing in six degrees of freedom. In general, human-cobot systems combine the best of human capabilities with the best of robot capabilities.

[ Northwestern ]

There’s something so soothing about watching a lithium battery get wrecked and burn for 8 minutes.

[ Hardcore Robotics ]

EELS, or Exobiology Extant Life Surveyor, is a versatile, snake-like robot designed for exploration of previously inaccessible terrain. This talk on EELS was presented at the 2024 Amazon MARS conference.

[ JPL ]

The convergence of AI and robotics will unlock a wonderful new world of possibilities in everyday life, says robotics and AI pioneer Daniela Rus. Diving into the way machines think, she reveals how “liquid networks”—a revolutionary class of AI that mimics the neural processes of simple organisms—could help intelligent machines process information more efficiently and give rise to “physical intelligence” that will enable AI to operate beyond digital confines and engage dynamically in the real world.

[ TED ]



What’s a secret to getting more students to participate in an IEEE society? Give them a seat at the table so they have a say in how the organization is run.

That’s what the IEEE Robotics and Automation Society has done. Budding engineers serve on the RAS board of directors, have voting privileges, and work within technical committees.

“They have been given a voice in how the society runs because, in the end, students are among the main beneficiaries,” says Enrica Tricomi, chair of the RAS’s student activities committee. The SAC is responsible for student programs and benefits. It also makes recommendations to the society’s board about new offerings.

A Guide for Inspiring the Next Generation Roboticists

The IEEE Robotics and Automation Society isn’t focused only on boosting its student membership. It also wants to get more young people interested in pursuing a robotics career. One way the society’s volunteers try to inspire the next generation of roboticists is through IEEE Spectrum’s award-winning Robots website. The interactive guide features more than 250 real-world robots, with thousands of photos, videos, and exclusive interactives, plus news and detailed technical specifications.

The site is designed for anyone interested in robotics, including expert and beginner enthusiasts, researchers, entrepreneurs, students, STEM educators, and other teachers.

Schools and students across the globe use the site. Volunteers on the RAS steering committee suggest robots to add, and they help support new content creation on the site.

“You feel listened to and valued whenever there are official decisions to be made, because the board also wants to know the perspective of students on how to offer benefits to the RAS members, especially for young researchers, since hopefully they will be the society’s future leaders,” says Tricomi, a bioengineer who is pursuing a Ph.D. in robotics at Heidelberg University, in Germany.

The society’s approach has paid off. Since 2018, student membership has grown by more than 50 percent to 5,436. The number of society chapters at student branches has increased from 312 in 2021 to 450.

The ability to express opinions isn’t the only reason students are joining, Tricomi says. The society recently launched several programs to engage them, including career fairs, travel grants, and networking opportunities with researchers.

Giving students leadership opportunities

As SAC chair, Tricomi is a voting member of RAS’s administrative committee, which oversees the society’s operations. She says having voting privileges shows “how important it is to the society to have student representation.”

“We receive a lot of support from the highest levels of the society, specifically the society president, Aude Billard, and past president Frank Chongwoo Park,” Tricomi says. “RAS boards have been rejuvenated to engage students even more and represent their voices. The chairs of these boards—including technical activities, conference activities, and publication activities—want to know the SAC chair and cochairs’ opinion on whether the new activities are benefiting students.”

Student members now can serve on IEEE technical committees that involve robotics in the role of student representatives.

That was an initiative from Kyujin Cho, IEEE Technical Activities vice president. Tricomi says the designation benefits young engineers because they learn about ongoing research in their field and because they have direct access to researchers.

Student representatives also help organize conference workshops.

The students had a hand in creating a welcome kit for conference attendees. The initiative, led by Amy Kyungwon Han, Technical Activities associate vice president, lists each day’s activities and their location.

“I think that all of us, especially those who are younger, can actively contribute and make a difference not only for the society and for ourselves but also for our peers.”

Being engaged with the technical topic in which the students work provides them with career growth, visibility in their field, and an opportunity to share their point of view with peers, Tricomi says.

“Being young, the first time that you express your opinion in public, you always feel uncomfortable because you don’t have much experience,” she says. “This is the opposite of the message the society wants to send. We want to listen to students’ voices because they are an important part of the society.”

Tricomi herself recently became a member of the Technical Activities board.

She joined, she says, because “this is kind of a technical family by choice. And you want to be active and contribute to your family, right? I think that all of us, especially those who are younger, can actively contribute and make a difference not only for the society and for ourselves but also for our peers.”

Job fairs and travel grants

Several new initiatives have been rolled out at the society’s flagship conferences. The meetings have always included onsite events for students to network with each other and to mingle with researchers over lunch. The events give the budding engineers an opportunity to talk with leaders they normally wouldn’t meet, Tricomi says.

“It’s much appreciated, especially by very young or shy students,” she says.

Some luncheons have included sessions on career advice from leaders in academia and industry, or from startup founders—giving the students a sense of what it’s like to work for such organizations.

Conferences now include career fairs, where students can meet with hiring companies.

The society also developed a software platform that allows candidates to upload their résumé onsite. If they are a match for an open position, interviews can be held on the spot.

A variety of travel grants have been made available to students with limited resources so they can present their research papers at the society’s major conferences. More than 200 travel grants were awarded to the 2023 IEEE International Conference on Robotics and Automation, Tricomi says.

“It’s very important for them to be there, presenting their work, gaining visibility, sharing their research, and also networking,” she says.

The new IDEA (inclusion, diversity, equity, and accessibility) travel grant for underrepresented groups was established by the society’s IEEE Women in Engineering committee and its chair, Karinne Ramirez Amaro. The grant can help students who are not presenters to attend conferences. It also helps increase diversity within the robotics field, Tricomi says.

The Member Support Program is a new initiative from the RAS member activities board’s vice president, Katja Mombaur, and past vice president Stefano Stramigioli. Financial support to attend the annual International Conference on Intelligent Robots and Systems is available to members and students who have contributed to the society’s mission-related activities. The projects include organizing workshops, discussions, lectures, or networking events at conferences or sponsored events; serving on boards or committees; or writing papers that were accepted for publication by conferences or journals.

The society also gets budding engineers involved in publication activities through its Young Reviewers Program, which introduces them to best practices for peer review. Senior reviewers assign the students papers to check and oversee their work.

Personal and professional growth opportunities

Tricomi joined the society in 2021 shortly after starting her Ph.D. program at Heidelberg. Her research is in wearable assistive robotics for human augmentation or rehabilitation purposes. She holds a master’s degree in biomedical engineering from Politecnico di Torino, in Italy.

She was new to the field of robotics, so her Ph.D. advisor, IEEE Senior Member Lorenzo Masia, encouraged her to volunteer for the society. She is now transitioning to the role of SAC senior chair, and she says she is eager to collaborate with the new team to promote student and early career engagement within the robotics field.

“I’ve realized I’ve grown up a lot in the two years since I started as chair,” she says. “At the beginning, I was much shier. I really want my colleagues to experience the same personal and professional growth as I have. You learn not only technical skills but also soft skills, which are very important in your career.”



Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

RoboCup German Open: 17–21 April 2024, KASSEL, GERMANYAUVSI XPONENTIAL 2024: 22–25 April 2024, SAN DIEGOEurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCEICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDS

Enjoy today’s videos!

In the SpaceHopper project, students at ETH Zurich developed a robot capable of moving in low gravity environments through hopping motions. It is intended to be used in future space missions to explore small celestial bodies.

The exploration of asteroids and moons could provide insights into the formation of the universe, and they may contain valuable minerals that humanity could use in the future.The project began in 2021 as an ETH focus project for bachelor’s students. Now, it is being continued as a regular research project. A particular challenge in developing exploration robots for asteroids is that, unlike larger celestial bodies like Earth, there is low gravity on asteroids and moons. The students have therefore tested their robot’s functionality in zero gravity during a parabolic flight. The parabolic flight was conducted in collaboration with the European Space Agency as part of the ESA Academy Experiments Programme.

[ SpaceHopper ]

It’s still kind of wild to me that it’s now possible to just build a robot like Menteebot. Having said that, at present it looks to be a fairly long way from being able to usefully do tasks in a reliable way.

[ Menteebot ]

Look, it’s the robot we all actually want!

[ Github ]

I wasn’t quite sure what made this building especially “robot-friendly” until I saw the DEDICATED ROBOT ELEVATOR.

[ NAVER ]

We are glad to announce the latest updates with our humanoid robot CL-1. In the test, it demonstrates stair climbing in a single stride based on real-time terrain perception. For the very first time, CL-1 accomplishes back and forth running, in a stable and dynamic way!

[ LimX Dynamics ]

EEWOC [Extended-reach Enhanced Wheeled Orb for Climbing] uses a unique locomotion scheme to climb complex steel structures with its magnetic grippers. Its lightweight and highly extendable tape spring limb can reach over 1.2 meters, allowing it to traverse gaps and obstacles much larger than other existing climbing robots. Its ability to bend allows it to reach around corners and over ledges, and it can transition between surfaces easily thanks to assistance from its wheels. The wheels also let it to drive more quickly and efficiently on the ground. These features make EEWOC well-suited for climbing the complex steel structures seen in real-world environments.

[ Paper ]

Thanks to its “buttock-contact sensors,” JSK’s musculoskeletal humanoid has mastered(ish) the chair-scoot.

[ University of Tokyo ]

Thanks, Kento!

Physical therapy seems like a great application for a humaonid robot when you don’t really need that humanoid robot to do much of anything.

[ Fourier Intelligence ]

NASA’s Ingenuity Mars helicopter became the first vehicle to achieve powered, controlled flight on another planet when it took to the Martian skies on 19 April 2021. This video maps the location of the 72 flights that the helicopter took over the course of nearly three years. Ingenuity far surpassed expectations—soaring higher and faster than previously imagined.

[ JPL ]

No thank you!

[ Paper ]

MERL introduces a new autonomous robotic assembly technology, offering an initial glimpse into how robots will work in future factories. Unlike conventional approaches where humans set pre-conditions for assembly, our technology empowers robots to adapt to diverse scenarios. We showcase the autonomous assembly of a gear box that was demonstrated live at CES2024.

[ Mitsubishi ]

Thanks, Devesh!

In November, 2023 Digit was deployed in a distribution center unloading totes from an AMR as part of regular facility operations, including a shift during Cyber Monday.

[ Agility ]

The PR2 just refuses to die. Last time I checked, official support for it ceased in 2016!

[ University of Bremen ]

DARPA’s Air Combat Evolution (ACE) program has achieved the first-ever in-air tests of AI algorithms autonomously flying a fighter jet against a human-piloted fighter jet in within-visual-range combat scenarios (sometimes referred to as “dogfighting”).In this video, team members discuss what makes the ACE program unlike other aerospace autonomy projects and how it represents a transformational moment in aerospace history, establishing a foundation for ethical, trusted, human-machine teaming for complex military and civilian applications.

[ DARPA ]

Sometimes robots that exist for one single purpose that they only do moderately successfully while trying really hard are the best of robots.

[ CMU ]



The paper delves into the significance of Secure Runtime Assurance (SRTA) for the operational integrity and safety of autonomous robotics groups, with a focus on drones. It presents a comprehensive view of how SRTA has evolved from traditional runtime assurance methods to address the dynamic and complex nature of autonomous systems. Through integrating artificial intelligence and machine learning, SRTA seeks to tackle the multifaceted challenges autonomous systems face, highlighting the need for adaptive, scalable, and secure solutions. Emphasizing a hierarchical approach to decision-making, the paper also highlights the critical role of redundancy in ensuring reliability and anticipates future advancements in RTA technologies. This paper reflects an ongoing effort to harmonize safety and efficiency within regulatory frameworks for autonomous robotics.



Stephen Cass: Hello and welcome to Fixing the Future, an IEEE Spectrum podcast where we look at concrete solutions to tough problems. I’m your host, Stephen Cass, a senior editor at IEEE Spectrum. And before I start, I just want to tell you that you can get the latest coverage of some of Spectrum’s most important beats, including AI, climate change, and robotics, by signing up for one of our free newsletters. Just go to spectrum.ieee.org/newsletters to subscribe. We’ve been covering the drone delivery company Zipline in Spectrum for several years, and I do encourage listeners to check out our great onsite reporting from Rwanda in 2019 when we visited one of Zipline’s dispatch centers for delivering vital medical supplies into rural areas. But now it’s 2024, and Zipline is expanding into commercial drone delivery in the United States, including into urban areas, and hitting some recent milestones. Here to talk about some of those milestones today, we have Keenan Wyrobek, Zipline’s co-founder and CTO. Keenan, welcome to the show.

Keenan Wyrobek: Great to be here. Thanks for having me.

Cass: So before we get into what’s going on with the United States, can you first catch us up on how things have been going on with Rwanda and the other African countries you’ve been operating in?

Wyrobek: Yeah, absolutely. So we’re now operating in eight countries, including here in the US. That includes a handful of countries in Africa, as well as Japan and Europe. So in Africa, it’s really exciting. So the scale is really impressive, basically. As we’ve been operating, started eight years ago with blood, then moved into vaccine delivery and delivering many other things in the healthcare space, as well as outside the healthcare space. We can talk a little bit about in things like animal husbandry and other things. The scale is really what’s exciting. We have a single distribution center there that now regularly flies more than the equivalent of once the equator of the Earth every day. And that’s just from one of a whole bunch of distribution centers. That’s where we are really with that operation today.

Cass: So could you talk a little bit about those non-medical systems? Because this was very much how we’d seen blood being parachuted down from these drones and reaching those distant centers. What other things are you delivering there?

Wyrobek: Yeah, absolutely. So start with blood, like you said, then vaccines. We’ve now done delivered well over 15 million vaccine doses, lots of other pharmaceutical use cases to hospitals and clinics, and more recently, patient home delivery for chronic care of things like hypertension, HIV-positive patients, and things like that. And then, yeah, moved into some really exciting use cases and things like animal husbandry. One that I’m personally really excited about is supporting these genetic diversity campaigns. It’s one of those things very unglamorous, but really impactful. One of the main sources of protein around the world is cow’s milk. And it turns out the difference between a non-genetically diverse cow and a genetically diverse cow can be 10x difference in milk production. And so one of the things we deliver is bull semen. We’re very good at the cold chain involved in that as we’ve mastered in vaccines and blood. And that’s just one of many things we’re doing in other spaces outside of healthcare directly.

Cass: Oh, fascinating. So turning now to the US, it seems like there’s been two big developments recently. One is you’re getting close to deploying Platform 2, which has some really fascinating tech that allows packages to be delivered very precisely by tether. And I do want to talk about that later. But first, I want to talk about a big milestone you had late last year. And this was something that goes by the very unlovely acronym of a BVLOS flight. Can you tell us what a BVLOS stands for and why that flight was such a big deal?

Wryobek: Yeah, “beyond visual line of sight.” And so that is basically, before this milestone last year, all drone deliveries, all drone operations in the US were done by people standing on the ground, looking at the sky, that line of sight. And that’s how basically we made sure that the drones were staying clear of aircraft. This is true of everybody. Now, this is important because in places like the United States, many aircraft don’t and aren’t required to carry a transponder, right? So transponders where they have a radio signal that they’re transmitting their location that our drones can listen to and use to maintain separation. And so the holy grail of basically scalable drone operations, of course, it’s physically impossible to have people standing around all the world staring at the sky, and is a sensing solution where you can sense those aircraft and avoid those aircraft. And this is something we’ve been working on for a long time and got the approval for late last year with the FAA, the first-ever use of sensors to detect and avoid for maintaining safety in the US airspace, which is just really, really exciting. That’s now been in operations in two distribution centers here, one in Utah and one in Arkansas ever since.

Cass: So could you just tell us a little bit about how that tech works? It just seems to be quite advanced to trust a drone to recognize, “Oh, that is an actual airplane that’s a Cessna that’s going to be here in about two minutes and is a real problem,” or, “No, it’s a hawk, which is just going about his business and I’m not going to ever come close to it at all because it’s so far away.

Wryobek: Yeah, this is really fun to talk about. So just to start with what we’re not doing, because most people expect us to use either a radar for this or cameras for this. And basically, those don’t work. And the radar, you would need such a heavy radar system to see 360 degrees all the way around your drone. And this is really important because two things to kind of plan in your mind. One is we’re not talking about autonomous driving where cars are close together. Aircraft never want to be as close together as cars are on a road, right? We’re talking about maintaining hundreds of meters of separation, and so you sense it a long distance. And drones don’t have right of way. So what that means is even if a plane’s coming up behind the drone, you got to sense that plane and get out of the way. And so to have enough radar on your drone that you can actually see far enough to maintain that separation in every direction, you’re talking about something that weighs many times the weight of a drone and it just doesn’t physically close. And so we started there because that’s sort of where we assumed and many people assume that’s the place to start. Then looked at cameras. Cameras have lots of drawbacks. And fundamentally, you can sort of-- we’ve all had this, you taken your phone and tried to take a picture of an airplane and you look at the picture, you can’t see the airplane. Yeah. It takes so many pixels of perfectly clean lenses to see an aircraft at a kilometer or two away that it really just is not practical or robust enough. And that’s when we went back to the drawing board and it ended up where we ended up, which is using an array of microphones to listen for aircraft, which works very well at very long distances to then maintain separation from those other aircraft.

Cass: So yeah, let’s talk about Platform 2 a little bit more because I should first explain for listeners who maybe aren’t familiar with Zipline that these are not the kind of the little purely sort of helicopter-like drones. These are these fixed wing with sort of loiter capability and hovering capabilities. So they’re not like your Mavic drones and so on. These have a capacity then for long-distance flight, which is what it gives them.

Wyrobek: Yeah. And maybe to jump into Platform 2— maybe starting with Platform 1, what does it look like? So Platform 1 is what we’ve been operating around the world for years now. And this basically looks like a small airplane, right? In the industry referred to as a fixed-wing aircraft. And it’s fixed wing because to solve the problem of going from a metro area to surrounding countryside, really two things matter. Your range and long range and low cost. And a fixed-wing aircraft over something that can hover has something like an 800% advantage in range and cost. And that’s why we did fix wing because it actually works for our customers for their needs for that use case. Platform 2 is all about, how do you deliver to homes and in metro areas where you need an incredible amount of precision to deliver to nearly every home. And so Platform 2—we call our drone zips—our drone, it flies out to the delivery site. Instead of floating a package down to a customer like Platform 1 does, it hovers. Platform 2 hovers and lowers down what we call a droid. And so the droids on tether. The drone stays way up high, about 100 meters up high, and the drone lowers down. And the drone itself-- sorry, the droid itself, it lowers down, it can fly. Right? So you think of it as like the tether does the heavy lifting, but the droid has fans. So if it gets hit by a gust of wind or whatnot, it can still stay very precisely on track and come in and deliver it to a very small area, put the package down, and then be out of there seconds later.

Cass: So let me get this right. Platform 2 is kind of as a combo, fixed wing and rotor wing. It’s like a VTOL like that. I’m cheating here a little bit because my colleague Evan Ackerman has a great Q&A on the Spectrum website with you, some of your team members about the nitty-gritty of how that design was evolved. But first off, it’s like a little droid thing at the end of the tether. How much extra precision do all those fans and stuff give you?

Wyrobek: Oh, massive, right? We can come down and hit a target within a few centimeters of where we want to deliver, which means we can deliver. Like if you have a small back porch, which is really common, right, in a lot of urban areas to have a small back porch or a small place on your roof or something like that, we can still just deliver as long as we have a few feet of open space. And that’s really powerful for being able to serve our customers. And a lot of people think of Platform 2 as like, “Hey, it’s a slightly better way of doing maybe a DoorDash-style operation, people in cars driving around.” And to be clear, it’s not slightly better. It’s massively better, much faster, more environmentally friendly. But we have many contracts for Platform 2 in the health space with US Health System Partners and Health Systems around the world. And what’s powerful about these customers in terms of their needs is they really need to serve all of their customers. And this is where a lot of our sort of-- this is where our engineering effort goes is how do you make a system that doesn’t just kind of work for some folks, and they can use it if they want to, but a health system is like, “No, I want this to work for everybody in my health network.” And so how do we get to that near 100 percent serviceability? And that’s what this droid really enables us to do. And of course, it has all these other magic benefits too. It makes some of the hardest design problems in this space much, much easier. The safety problem gets much easier by keeping the drone way up high.

Cass: Yeah, how high is Platform 2 hovering when it’s doing its deliveries?

Wyrobek: About 100 meters, so 300 plus feet, right? We’re talking about high up as a football field is long. And so it’s way up there. And it also helps with things like noise, right? We don’t want to live in a future where drones are all around us sounding like swarms of insects. We want drones to make no noise. We want them to just melt into the background. And so it makes that kind of problem much easier as well. And then, of course, the droid gets other benefits where for many products, we don’t need any packaging at all. We can just deliver the product right onto a table in your porch. And not just from a cost perspective, but again, from— we’re all familiar with the nightmare of packaging from deliveries we get. Eliminating packaging just has to be our future. And we’re really excited to advance that future.

Cass: From Evan’s Q&A, I know that a lot of effort went into making the droid element look rather adorable. Why was that so important?

Wryobek: Yeah, I like to describe it as sort of a cross between three things, if you kind of picture this, like a miniature little fan boat, right, because it has some fan, a big fan on the back, looks like a little fan boat, combined with sort of a baby seal, combined with a toaster. It sort of has that look to it. And making it adorable, there’s a bunch of sort of human things that matter, right? I want this to be something that when my grandmother, who’s not a tech-savvy, gets these deliveries, it’s approachable. It doesn’t come off as sort of scary. And when you make something cute, not only does it feel approachable, but it also forces you to get the details right so it is approachable, right? The rounded corners, right? This sounds really benign, but a lot of robots, it turns out if you bump into them, they scratch you. And we want you to be able to bump into this droid, and this is no big deal. And so getting the surfaces right, getting them— the surface is made sort of like a helmet foam. If you can picture that, right? The kind of thing you wouldn’t be afraid to touch if it touched you. And so getting it both to be something that feels safe, but is something that actually is safe to be around, those two things just matter a lot. Because again, we’re not designing this for some piloty kind of low-volume thing. Our customers want this in phenomenal volume. And so we really want this to be something that we’re all comfortable around.

Cass: Yeah, and one thing I want to pull out from that Q&A as well is it was an interesting note, because you mentioned it has three fans, but they’re rather unobtrusive. And the original design, you had two big fans on the sides, which was very great for maneuverability. But you had to get rid of those and come up with a three-fan design. And maybe you can explain why that was so.

Wryobek: Yeah, that’s a great detail. So the original design, the picture, it was like, imagine the package in the middle, and then kind of on either side of the package, two fans. So when you looked at it, it kind of looked like— I don’t know. It kind of looked like the package had big mouse ears or something. And when you looked at it, everybody had the same reaction. You kind of took this big step back. It was like, “Whoa, there’s this big thing coming down into my yard.” And when you’re doing this kind of user testing, we always joke, you don’t need to bring users in if it already makes you take a step back. And this is one of those things where like, “That’s just not good enough, right, to even start with that kind of refined design.” But when we got the sort of profile of it smaller, the way we think about it from a design experiment perspective is we want to deliver a large package. So basically, the droid needs to be as sucked down as small additional volume around that package as possible. So we spent a lot of time figuring out, “Okay, how do you do that sort of physically and aesthetically in a way that also gets that amazing performance, right? Because when I say performance, what I’m talking about is we still need it to work when the winds are blowing really hard outside and still can deliver precisely. And so it has to have a lot of aero performance to do that and still deliver precisely in essentially all weather conditions.

Cass: So I guess I just want to ask you then is, what kind of weight and volume are you able to deliver with this level of precision?

Wryobek: Yeah, yeah. So we’ll be working our way up to eight pounds. I say working our way up because that’s part of, once you launch a product like this, there’s refinement you can do overtime on many layers, but eight pounds, which was driven off, again, these health use cases. So it does basically 100 percent of what our health partners need to do. And it turns out it’s, nearly 100 percent of what we want to do in meal delivery. And even in the goods sector, I’m impressed by the percentage of goods we can deliver. One of our partners we work with, we can deliver over 80 percent of what they have in their big box store. And yeah, it’s wildly exceeding expectations on nearly every axis there. And volume, it’s big. It’s bigger than a shoebox. I don’t have a great-- I’m trying to think of a good reference to kind of bring it to life. But it looks like a small cooler basically inside. And it can comfortably fit a meal for four to give you a sense of the amount of food you can fit in there. Yeah.

Cass: So we’ve seen this history of Zipline in rural areas, and now we’re talking about expanding operations in more urban areas, but just how urban? I don’t imagine that we’ll see the zip lines of zooming around, say, the very hemmed-in streets, say, here in Midtown Manhattan. So what level of urban are we talking about?

Wryobek: Yeah, so the way we talk about it internally in our design process is basically we call three-story sprawl. Manhattan is the place where when we think of New York, we’re not talking about Manhattan, but most of the rest of New York, we are talking about it, right? Like the Bronx, things like that. We just have this sort of three stories forever. And that’s a lot of the world out here in California, that’s most of San Francisco. I think it’s something like 98 percent of San Francisco is that. If you’ve ever been to places like India and stuff like that, the cities, it’s just sort of this three stories going for a really long way. And that’s what we’re really focused on. And that’s also where we provide that incredible value because that’s also matches where the hardest traffic situations and things like that can make any other sort of terrestrial on-demand delivery be phenomenally late.

Cass: Well, no, I live out in Queens, so I agree there’s not much skyscrapers out there. Although there are quite a few trees and so on, but at the same time, there’s usually some sort of sidewalk availability. So is that kind of what you’re hoping to get into?

Wyrobek: Exactly. So as long as you’ve got a porch with a view of the sky or an alley with a view of the sky, it can be literally just a few feet, we can get in there, make a delivery, and be on our way.

Cass: And so you’ve done this preliminary test with the FAA, the BVLOS test, and so on. How close do you think you are to, and you’re working with a lot of partners, to really seeing this become routine commercial operations?

Wyrobek: Yeah, yeah. So at relatively limited scale, our operations here in Utah and in Arkansas that are leveraging that FAA approval for beyond visual line-of-sight flight operations, that’s been all day, every day now since our approval last year. With Platform 2, we’re really excited. That’s coming later this year. We’re currently in the phase of basically massive-scale testing. So we now have our production hardware and we’re taking it through a massive ground testing campaign. So this picture dozens of thermal chambers and five chambers and things like that just running to really both validate that we have the reliability we need and flush out any issues that we might have missed so we can address that difference between what we call the theoretical reliability and the actual reliability. And that’s running in parallel to a massive flight test campaign. Same idea, right? We’re slowly ramping up the flight volume as we fly into heavier conditions really to make sure we know the limits of the system. We know its actual reliability and true scaled operations so we can get the confidence that it’s ready to operate for people.

Cass: So you’ve got Platform 2. What’s kind of next on your technology roadmap for any possible platform three?

Wyrobek: Oh, great question. Yeah, I can’t comment on platform three at this time, but. And I will also say, Zipline is pouring our heart into Platform 2 right now. Getting Platform 2 ready for this-- the way I like to talk about this internally is today, we fly about four times the equator of the Earth in our operations on average. And that’s a few thousand flights per day. But the demand we have is for more like millions of flights per day, if not beyond. And so on the log scale, right, we’re halfway there. Three hours of magnitude down, three more zeros to come. And the level of testing, the level of systems engineering, the level of refinement required to do that is a lot. And there’s so many systems from weather forecasting to our onboard autonomy and our fleet management systems. And so to highlight one team, our system test team run by this really impressive individual named Juan Albanell, this team has taken us from where we were two years ago, where we had shown the concept at a very prototype stage of this delivery experience, and we’ve done the first order math kind of on the architecture and things like that through the iterations in test to actually make sure we had a drone that could actually fly in all these weather conditions with all the robustness and tolerance required to actually go to this global scale that Platform 2 is targeting.

Cass: Well, that’s fantastic. Well, I think there’s a lot more to talk about to come up in the future, and we look forward to talking with Zipline again. But for today, I’m afraid we’re going to have to leave it there. But it was really great to have you on the show, Keenan. Thank you so much.

Wyrobek: Cool. Absolutely, Stephen. It was a pleasure to speak with you.

Cass: So today on Fixing the Future, we were talking with Zipline’s Keenan Wyrobek about the progress of commercial drone deliveries. For IEEE Spectrum, I’m Stephen Cass, and I hope you’ll join us next time.



Boston Dynamics has just introduced a new Atlas humanoid robot, replacing the legendary hydraulic Atlas and intended to be a commercial product. This is huge news from the company that has spent the last decade building the most dynamic humanoids that the world has ever seen, and if you haven’t read our article about the announcement (and seen the video!), you should do that right now.

We’ve had about a decade of pent-up questions about an all-electric productized version of Atlas, and we were lucky enough to speak with Boston Dynamics CEO Robert Playter to learn more about where this robot came from and how it’s going to make commercial humanoid robots (finally) happen.

Robert Playter was the Vice President of Engineering at Boston Dynamics starting in 1994, which I’m pretty sure was back when Boston Dynamics still intended to be a modeling and simulation company rather than a robotics company. Playter became the CEO in 2019, helping the company make the difficult transition from R&D to commercial products with Spot, Stretch, and now (or very soon) Atlas.

We talked with Playter about what the heck took Boston Dynamics so long to make this robot, what the vision is for Atlas as a product, all that extreme flexibility, and what comes next.

Robert Playter on:

IEEE Spectrum: So what’s going on?

Robert Playter: Boston Dynamics has built an all-electric humanoid. It’s our newest generation of what’s been an almost 15-year effort in developing humanoids. We’re going to launch it as a product, targeting industrial applications, logistics, and places that are much more diverse than where you see Stretch—heavy objects with complex geometry, probably in manufacturing type environments. We’ve built our first robot, and we believe that’s really going to set the bar for the next generation of capabilities for this whole industry.

What took you so long?!

Playter: Well, we wanted to convince ourselves that we knew how to make a humanoid product that can handle a great diversity of tasks—much more so than our previous generations of robots—including at-pace bimanual manipulation of the types of heavy objects with complex geometry that we expect to find in industry. We also really wanted to understand the use cases, so we’ve done a lot of background work on making sure that we see where we can apply these robots fruitfully in industry.

We’ve obviously been working on this machine for a while, as we’ve been doing parallel development with our legacy Atlas. You’ve probably seen some of the videos of Atlas moving struts around—that’s the technical part of proving to ourselves that we can make this work. And then really designing a next generation machine that’s going to be an order of magnitude better than anything the world has seen.

“We’re not anxious to just show some whiz-bang tech, and we didn’t really want to indicate our intent to go here until we were convinced that there is a path to a product.” Robert Playter, Boston Dynamics

With Spot, it felt like Boston Dynamics developed the product first, without having a specific use case in mind: you put the robot out there and let people discover what it was good for. Is your approach different with Atlas?

Playter: You’re absolutely right. Spot was a technology looking for a product, and it’s taken time for us to really figure out the product market fit that we have in industrial inspection. But the challenge of that experience has left us wiser about really identifying the target applications before you say you’re going to build these things at scale.

Stretch is very different, because it had a clear target market. Atlas is going to be more like Stretch, although it’s going to be way more than a single task robot, which is kind of what Stretch is. Convincing ourselves that we could really generalize with Atlas has taken a little bit of time. This is going to be our third product in about four years. We’ve learned so much, and the world is different from that experience.

[back to top]

Is your vision for Atlas one of a general purpose robot?

Playter: It definitely needs to be a multi-use case robot. I believe that because I don’t think there’s very many examples where a single repetitive task is going to warrant these complex robots. I also think, though, that the practical matter is that you’re going to have to focus on a class of use cases, and really making them useful for the end customer. The lesson we’ve learned with both Spot and Stretch is that it’s critical to get out there and actually understand what makes this robot valuable to customers while making sure you’re building that into your development cycle. And if you can start that before you’ve even launched the product, then you’ll be better off.

[back to top]

How does thinking of this new Atlas as a product rather than a research platform change things?

Playter: I think the research that we’ve done over the past 10 or 15 years has been essential to making a humanoid useful in the first place. We focused on dynamic balancing and mobility and being able to pick something up and still maintain that mobility—those were research topics of the past that we’ve now figured out how to manage and are essential, I think, to doing useful work. There’s still a lot of work to be done on generality, so that humanoids can pick up any one of a thousand different parts and deal with them in a reasonable way. That level of generality hasn’t been proven yet; we think there’s promise, and that AI will be one of the tools that helps solve that. And there’s still a lot of product prototyping and iteration that will come out before we start building massive numbers of these things and shipping them to customers.

“This robot will be stronger at most of its joints than a person, and even an elite athlete, and will have a range of motion that exceeds anything a person can ever do.” —Robert Playter, Boston Dynamics

For a long time, it seemed like hydraulics were the best way of producing powerful dynamic motions for robots like Atlas. Has that now changed?

Playter: We first experimented with that with the launch of Spot. We had the same issue years ago, and discovered that we could build powerful lightweight electric motors that had the same kind of responsiveness and strength, or let’s say sufficient responsiveness and strength, to really make that work. We’ve designed an even newer set of really compact actuators into our electric Atlas, which pack the strength of essentially an elite human athlete into these tiny packages that make an electric humanoid feasible for us. So, this robot will be stronger at most of its joints than a person, and even an elite athlete, and will have a range of motion that exceeds anything a person can ever do. We’ve also compared the strength of our new electric Atlas to our hydraulic Atlas, and the electric Atlas is stronger.

[back to top]

In the context of Atlas’ range of motion, that introductory video was slightly uncomfortable to watch, which I’m sure was deliberate. Why introduce the new Atlas in that way?

Playter: These high range of motion actuators are going to enable a unique set of movements that ultimately will let the robot be very efficient. Imagine being able to turn around without having to take a bunch of steps to turn your whole body instead. The motions we showed [in the video] are ones where our engineers were like, “hey, with these joints, we could get up like this!” And it just wasn’t something we had that really thought about before. This flexibility creates a palette that you can design new stuff on, and we’re already having fun with it and we decided we wanted to share that excitement with the world.

[back to top]

“Everybody will buy one robot—we learned that with Spot. But they won’t start by buying fleets, and you don’t have a business until you can sell multiple robots to the same customer.” Robert Playter, Boston Dynamics

This does seem like a way of making Atlas more efficient, but I’ve heard from other folks working on humanoids that it’s important for robots to move in familiar and predictable ways for people to be comfortable working around them. What’s your perspective on that?

Playter: I do think that people are going to have to become familiar with our robot; I don’t think that means limiting yourself to human motions. I believe that ultimately, if your robot is stronger or more flexible, it will be able to do things that humans can’t do, or don’t want to do.

One of the real challenges of making a product useful is that you’ve got to have sufficient productivity to satisfy a customer. If you’re slow, that’s hard. We learned that with Stretch. We had two generations of Stretch, and the first generation did not have a joint that let it pivot 180 degrees, so it had to ponderously turn around between picking up a box and dropping it off. That was a killer. And so we decided “nope, gotta have that rotational joint.” It lets Stretch be so much faster and more efficient. At the end of the day, that’s what counts. And people will get used to it.

What can you tell me about the head?

Boston Dynamics CEO Robert Playter said the head on the new Atlas robot has been designed not to mimic the human form but rather “to project something else: a friendly place to look to gain some understanding about the intent of the robot.”Boston Dynamics

Playter: The old Atlas did not have an articulated head. But having an articulated head gives you a tool that you can use to indicate intent, and there are integrated lights which will be able to communicate to users. Some of our original concepts had more of a [human] head shape, but for us they always looked a little bit threatening or dystopian somehow, and we wanted to get away from that. So we made a very purposeful decision about the head shape, and our explicit intent was for it not to be human-like. We’re trying to project something else: a friendly place to look to gain some understanding about the intent of the robot.

The design borrows from some friendly shapes that we’d seen in the past. For example, there’s the old Pixar lamp that everybody fell in love with decades ago, and that informed some of the design for us.

[back to top]

How do you think the decade(s) of experience working on humanoids as well as your experience commercializing Spot will benefit you when it comes to making Atlas into a product?

Playter: This is our third product, and one of the things we’ve learned is that it takes way more than some interesting technology to make a product work. You have to have a real use case, and you have to have real productivity around that use case that a customer cares about. Everybody will buy one robot—we learned that with Spot. But they won’t start by buying fleets, and you don’t have a business until you can sell multiple robots to the same customer. And you don’t get there without all this other stuff—the reliability, the service, the integration.

When we launched Spot as a product several years ago, it was really about transforming the whole company. We had to take on all of these new disciplines: manufacturing, service, measuring the quality and reliability of our robots and then building systems and tools to make them steadily better. That transformation is not easy, but the fact that we’ve successfully navigated through that as an organization means that we can easily bring that mindset and skill set to bear as a company. Honestly, that transition takes two or three years to get through, so all of the brand new startup companies out there who have a prototype of a humanoid working—they haven’t even begun that journey.

There’s also cost. Building something effectively at a reasonable cost so that you can sell it at a reasonable cost and ultimately make some money out of it, that’s not easy either. And frankly, without the support of Hyundai which is of course a world-class manufacturing expert, it would be really challenging to do it on our own.

So yeah, we’re much more sober about what it takes to succeed now. We’re not anxious to just show some whiz-bang tech, and we didn’t really want to indicate our intent to go here until we were convinced that there is a path to a product. And I think ultimately, that will win the day.

[back to top]

What will you be working on in the near future, and what will you be able to share?

Playter: We’ll start showing more of the dexterous manipulation on the new Atlas that we’ve already shown on our legacy Atlas. And we’re targeting proof of technology testing in factories at Hyundai Motor Group [HMG] as early as next year. HMG is really excited about this venture; they want to transform their manufacturing and they see Atlas as a big part of that, and so we’re going to get on that soon.

[back to top]

What do you think other robotics folks will find most exciting about the new Atlas?

Playter: Having a robot with so much power and agility packed into a relatively small and lightweight package. I’ve felt honored in the past that most of these other companies compare themselves to us. They say, “well, where are we on the Boston Dynamics bar?” I think we just raised the bar. And that’s ultimately good for the industry, right? People will go, “oh, wow, that’s possible!” And frankly, they’ll start chasing us as fast as they can—that’s what we’ve seen so far. I think it’ll end up pulling the whole industry forward.



Yesterday, Boston Dynamics bid farewell to the iconic Atlas humanoid robot. Or, the hydraulically-powered version of Atlas, anyway—if you read between the lines of the video description (or even just read the actual lines of the video description), it was pretty clear that although hydraulic Atlas was retiring, it wasn’t the end of the Atlas humanoid program at Boston Dynamics. In fact, Atlas is already back, and better than ever.

Today, Boston Dynamics is introducing a new version of Atlas that’s all-electric. It’s powered by batteries and electric actuators, no more messy hydraulics. It exceeds human performance in terms of both strength and flexibility. And for the first time, Boston Dynamics is calling this humanoid robot a product. We’ll take a look at everything that Boston Dynamics is announcing today, and have even more detail in this Q&A with Boston Dynamics CEO Robert Playter.

Boston Dynamics’ new electric humanoid has been simultaneously one of the worst and best kept secrets in robotics over the last year or so. What I mean is that it seemed obvious, or even inevitable, that Boston Dynamics would take the expertise in humanoids that it developed with Atlas and combine that with its experience productizing a fully electric system like Spot. But just because something seems inevitable doesn’t mean it actually is inevitable, and Boston Dynamics has done an admirable job of carrying on as normal while building a fully electric humanoid from scratch. And here it is:


It’s all new, it’s all electric, and some of those movements make me slightly uncomfortable (we’ll get into that in a bit). The blog post accompanying the video is sparse on technical detail, but let’s go through the most interesting parts:

A decade ago, we were one of the only companies putting real R&D effort into humanoid robots. Now the landscape in the robotics industry is very different.

In 2010, we took a look at all the humanoid robots then in existence. You could, I suppose, argue that Honda was putting real R&D effort into ASIMO back then, but yeah, pretty much all those other humanoid robots came from research rather than industry. Now, it feels like we’re up to our eyeballs in commercial humanoids, but over the past couple of years, as startups have appeared out of nowhere with brand new humanoid robots, Boston Dynamics (to most outward appearances) was just keepin’ on with that R&D. Today’s announcement certainly changes that.

We are confident in our plan to not just create an impressive R&D project, but to deliver a valuable solution. This journey will start with Hyundai—in addition to investing in us, the Hyundai team is building the next generation of automotive manufacturing capabilities, and it will serve as a perfect testing ground for new Atlas applications.

Boston Dynamics

This is a significant advantage for Boston Dynamics—through Hyundai, they can essentially be their own first customer for humanoid robots, offering an immediate use case in a very friendly transitional environment. Tesla has a similar advantage with Optimus, but Boston Dynamics also has experience sourcing and selling and supporting Spot, which are those business-y things that seem like they’re not the hard part until they turn out to actually be the hard part.

In the months and years ahead, we’re excited to show what the world’s most dynamic humanoid robot can really do—in the lab, in the factory, and in our lives.

World’s most dynamic humanoid, you say? Awesome! Prove it! On video! With outtakes!

The electric version of Atlas will be stronger, with a broader range of motion than any of our previous generations. For example, our last generation hydraulic Atlas (HD Atlas) could already lift and maneuver a wide variety of heavy, irregular objects; we are continuing to build on those existing capabilities and are exploring several new gripper variations to meet a diverse set of expected manipulation needs in customer environments.

Now we’re getting to the good bits. It’s especially notable here that the electric version of Atlas will be “stronger” than the previous hydraulic version, because for a long time hydraulics were really the only way to get the kind of explosively powerful repetitive dynamic motions that enabled Atlas to do jumps and flips. And the switch away from hydraulics enables that extra range of motion now that there aren’t hoses and stuff to deal with.

It’s also pretty clear that the new Atlas is built to continue the kind of work that hydraulic Atlas has been doing, manipulating big and heavy car parts. This is in sharp contrast to most other humanoid robots that we’ve seen, which have primarily focused on moving small objects or bins around in warehouse environments.


We are not just delivering industry-leading hardware. Some of our most exciting progress over the past couple of years has been in software. In addition to our decades of expertise in simulation and model predictive control, we have equipped our robots with new AI and machine learning tools, like reinforcement learning and computer vision to ensure they can operate and adapt efficiently to complex real-world situations.

This is all par for the course now, but it’s also not particularly meaningful without more information. “We will give our robots new capabilities through machine learning and AI” is what every humanoid robotics company (and most other robotics companies) are saying, but I’m not sure that we’re there yet, because there’s an “okay but how?” that needs to happen first. I’m not saying that it won’t happen, just pointing out that until it does happen, it hasn’t happened.

The humanoid form factor is a useful design for robots working in a world designed for people. However, that form factor doesn’t limit our vision of how a bipedal robot can move, what tools it needs to succeed, and how it can help people accomplish more.

Agility Robotics has a similar philosophy with Digit, which has a mostly humanoid form factor to operate in human environments but also uses a non-human leg design because Agility believes that it works better. Atlas is a bit more human-like with its overall design, but there are some striking differences, including both range of motion and the head, both of which we’ll be talking more about.

We designed the electric version of Atlas to be stronger, more dexterous, and more agile. Atlas may resemble a human form factor, but we are equipping the robot to move in the most efficient way possible to complete a task, rather than being constrained by a human range of motion. Atlas will move in ways that exceed human capabilities.

The introductory video with the new Atlas really punches you in the face with this: Atlas is not constrained by human range of motion and will leverage its extra degrees of freedom to operate faster and more efficiently, even if you personally might find some of those motions a little bit unsettling.

Boston Dynamics

Combining decades of practical experience with first principles thinking, we are confident in our ability to deliver a robot uniquely capable of tackling dull, dirty, and dangerous tasks in real applications.

As Marco Hutter pointed out, most commercial robots (humanoids included) are really only targeting tasks that are dull, because dull usually means repetitive, and robots are very good at repetitive. Dirty is a little more complicated, and dangerous is a lot more complicated than that. I appreciate that Boston Dynamics is targeting those other categories of tasks from the outset.

Commercialization takes great engineering, but it also takes patience, imagination, and collaboration. Boston Dynamics has proven that we can deliver the full package with both industry-leading robotics and a complete ecosystem of software, services, and support to make robotics useful in the real world.

There’s a lot more to building a successful robotics company than building a successful robot. Arguably, building a successful robot is not even the hardest part, long term. Having over 1500 Spot robots deployed with customers gives them a well-established product infrastructure baseline to expand from with the new Atlas.

Taking a step back, let’s consider the position that Boston Dynamics is in when it comes to the humanoid space right now.

The new Atlas appears to be a reasonably mature platform with explicit commercial potential, but it’s not yet clear if this particular version of Atlas is truly commercially viable, in terms of being manufacturable and supportable at scale—it’s Atlas 001, after all. There’s likely a huge amount of work that still needs to be done, but it’s a process that the company has already gone through with Spot. My guess is that Boston Dynamics has some catching up to do with respect to other humanoid companies that are already entering pilot projects.

In terms of capabilities, even though the new Atlas hardware is new, it’s not like Boston Dynamics is starting from scratch, since they’re already transferring skills from hydraulic Atlas onto the new platform. But, we haven’t seen the new Atlas doing any practical tasks yet, so it’s hard to tell how far along that is, and it would be premature to assume that hydraulic Atlas doing all kinds of amazing things in YouTube videos implies that electric Atlas can do similar things safely and reliably in a product context. There’s a gap there, possibly an enormous gap, and we’ll need to see more from the new Atlas to understand where it’s at.

And obviously, there’s a lot of competition in humanoids right now, although I’d like to think that the potential for practical humanoid robots to be useful in society is significant enough that there will be room for lots of different approaches. Boston Dynamics was very early to humanoids in general, but they’re somewhat late to this recent (and rather abrupt) humanoid commercialization push. This may not be a problem, especially if Atlas is targeting applications where its strength and flexibility sets it apart from other robots in the space, and if their depth of experience deploying commercial robotic platforms helps them to scale quickly.

Boston Dynamics

An electric Atlas may indeed have been inevitable, and it’s incredibly exciting to (finally!) see Boston Dynamics take this next step towards a commercial humanoid, which would deliver on more than a decade of ambition stretching back through the DARPA Robotics Challenge to PETMAN. We’ve been promised more manipulation footage soon, and Boston Dynamics expects that Atlas will be in the technology demonstration phase in Hyundai factories as early as next year.

We have a lot more questions, but we have a lot more answers, too: you’ll find a Q&A with Boston Dynamics CEO Robert Playter right here.



In a new video posted today, Boston Dynamics is sending off its hydraulic Atlas humanoid robot. “For almost a decade,” the video description reads, “Atlas has sparked our imagination, inspired the next generations of roboticists, and leapt over technical barriers in the field. Now it’s time for our hydraulic Atlas robot to kick back and relax.”

Hydraulic Atlas has certainly earned some relaxation; Boston Dynamics has been absolutely merciless with its humanoid research program. This isn’t a criticism—sometimes being merciless to your hardware is necessary to push the envelope of what’s possible. And as spectators, we just just get to enjoy it, and this highlight reel includes unseen footage of Atlas doing things well along with unseen footage of Atlas doing things not so well. Which, let’s be honest, is what we’re all really here for.

There’s so much more to the history of Atlas than this video shows. Atlas traces its history back to a DARPA project called PETMAN (Protection Ensemble Test Mannequin), which we first wrote about in 2009, so long ago that we had to dig up our own article on the Wayback Machine. As contributor Mikell Taylor wrote back then:

PETMAN is designed to test the suits used by soldiers to protect themselves against chemical warfare agents. It has to be capable of moving just like a soldier—walking, running, bending, reaching, army crawling—to test the suit’s durability in a full range of motion. To really simulate humans as accurately as possible, PETMAN will even be able to “sweat”.

Relative to the other humanoid robots out there at the time (the most famous of which, by far, was Honda’s ASIMO), PETMAN’s movement and balance were very, very impressive. Also impressive was the presumably unintentional way in which this PETMAN video synced up with the music video to Stayin’ Alive by the Bee Gees. Anyway, DARPA was suitably impressed by all this impressiveness, and chose Boston Dynamics to build another humanoid robot to be used for the DARPA Robotics Challenge. That robot was unveiled ten years ago.

The DRC featured a [still looking for a collective noun for humanoid robots] of Atlases, and it seemed like Boston Dynamics was hooked on the form factor, because less than a year after the DRC Finals the company announced the next generation of Atlas, which could do some useful things like move boxes around. Every six months or so, Boston Dynamics put out a new Atlas video, with the robot running or jumping or dancing or doing parkour, leveraging its powerful hydraulics to impress us every single time. There was really nothing like hydraulic Atlas in terms of dynamic performance, and you could argue that there still isn’t. This is a robot that will be missed.

The original rendering of Atlas, followed by four generations of the robot.Boston Dynamics/IEEE Spectrum

Now, if you’re wondering why Boston Dynamics is saying “it’s time for our hydraulic Atlas robot to kick back and relax,” rather than just “our Atlas robot,” and if you’re also wondering why the video description ends with “take a look back at everything we’ve accomplished with the Atlas platform “to date,” well, I can’t help you. Some people might attempt to draw some inferences and conclusions from that very specific and deliberate language, but I would certainly not be one of them, because I’m well known for never speculating about anything.

I would, however, point out a few things that have been obvious for a while now. Namely, that:

  • Boston Dynamics has been focusing fairly explicitly on commercialization over the past several years
  • Complex hydraulic robots are not product friendly because (among other things) they tend to leave puddles of hydraulic fluid on the carpet
  • Boston Dynamics has been very successful with Spot as a productized electric platform based on earlier hydraulic research platforms
  • Fully electric commercial humanoids really seems to be where robotics is at right now
There’s nothing at all new in any of this; the only additional piece of information we have is that the hydraulic Atlas is, as of today, retiring. And I’m just going to leave things there.


Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

RoboCup German Open: 17–21 April 2024, KASSEL, GERMANYAUVSI XPONENTIAL 2024: 22–25 April 2024, SAN DIEGOEurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCEICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDSCybathlon 2024: 25–27 October 2024, ZURICH

Enjoy today’s videos!

I think suggesting that robots can’t fall is much less useful than instead suggesting that robots can fall and get quickly and easily get back up again.

[ Deep Robotics ]

Sanctuary AI says that this video shows Phoenix operating at “human-equivalent speed,” but they don’t specify which human or under which conditions. Though it’s faster than I would be, that’s for sure.

[ Sanctuary AI ]

“Suzume” is an animated film by Makoto Shinkai, in which one of the characters gets turned into a three-legged chair:

Shintaro Inoue from JSK Lab at the University of Tokyo has managed to build a robotic version of that same chair, which is pretty impressive:


[ Github ]

Thanks, Shintaro!

Humanoid robot EVE training for home assistance like putting groceries into the kitchen cabinets.

[ 1X ]

This is the RAM—robotic autonomous mower. It can be dropped anywhere in the world and will wake up with a mission to make tall grass around it shorter. Here is a quick clip of it working on the Presidio in SF.

[ Electric Sheep ]

This year, our robots braved a Finnish winter for the first time. As the snow clears and the days get longer, we’re looking back on how our robots made thousands of deliveries to S Group customers during the colder months.

[ Starship ]

Agility Robotics is doing its best to answer the (very common) question of “Okay, but what can humanoid robots actually do?”


[ Agility Robotics ]

Digit is great and everything, but Cassie will always be one of my favorite robots.

[ CoRIS ]

Adopting omnidirectional Field of View (FoV) cameras in aerial robots vastly improves perception ability, significantly advancing aerial robotics’s capabilities in inspection, reconstruction, and rescue tasks. We propose OmniNxt, a fully open-source aerial robotics platform with omnidirectional perception.

[ OmniNxt ]

The MAkEable framework enhances mobile manipulation in settings designed around humans by streamlining the process of sharing learned skills and experiences among different robots and contexts. Practical tests confirm its efficiency in a range of scenarios, involving different robots, in tasks such as object grasping, coordinated use of both hands in tasks, and the exchange of skills among humanoid robots.

[ Paper ]

We conducted trials of Ringbot outdoors on a 400 meter track. With a power source of 2300 milliamp-hours and 11.1 Volts, Ringbot managed to cover approximately 3 kilometers in 37 minutes. We commanded its target speed and direction using a remote joystick controller (Steam Deck), and Ringbot experienced five falls during this trial.

[ Paper ]

There is a notable lack of consistency about where exactly Boston Dynamics wants you to think Spot’s eyes are.

[ Boston Dynamics ]

As with every single cooking video, there’s a lot of background prep that’s required for this robot to cook an entire meal, but I would utterly demolish those fries.

[ Dino Robotics ]

Here’s everything you need to know about Wing delivery drones, except for how much human time they actually require and the true cost of making deliveries by drone, because those things aren’t fun to talk about.

[ Wing ]

This CMU Teruko Yata Memorial Lecture is by Agility Robotics’ Jonathan Hurst, on “Human-Centric Robots and How Learning Enables Generality.”

Humans have dreamt of robot helpers forever. What’s new is that this dream is becoming real. New developments in AI, building on foundations of hardware and passive dynamics, enable vastly improved generality. Robots can step out of highly structured environments and become more human-centric: operating in human spaces, interacting with people, and doing some basic human workflows. By connecting a Large Language Model, Digit can convert natural language high-level requests into complex robot instructions, composing the library of skills together, using human context to achieve real work in the human world. All of this is new—and it is never going back: AI will drive a fast-following robot revolution that is going to change the way we live.

[ CMU ]



We tend to think about hopping robots from the ground up. That is, they start on the ground, and then, by hopping, incorporate a aerial phase into their locomotion. But there’s no reason why aerial robots can’t approach hopping from the other direction, by adding a hopping ground phase to flight. Hopcopter is the first robot that I’ve ever seen give this a try, and it’s remarkably effective, combining a tiny quadrotor with a springy leg to hop hop hop all over the place.

Songnan Bai, Runze Ding, Song Li, and Bingxuan Pu

So why in the air is it worth adding a pogo stick to an otherwise perfectly functional quadrotor? Well, flying is certainly a valuable ability to have, but does take a lot of energy. If you pay close attention to birds (acknowledged experts in the space), they tend to spend a substantial amount of time doing their level best not to fly, often by walking on the ground or jumping around in trees. Not flying most of the time is arguably one of the things that makes birds so successful—it’s that multimodal locomotion capability that has helped them to adapt to so many different environments and situations.

Hopcopter is multimodal as well, although in a slightly more restrictive sense: Its two modes are flying and intermittent flying. But the intermittent flying is very important, because cutting down on that flight phase gives Hopcopter some of the same efficiency benefits that birds experience. By itself, a quadrotor of hopcopter’s size can stay airborne for about 400 seconds, while Hopcopter can hop continuously for more than 20 minutes. If your objective is to cover as much distance as possible, Hopcopter might not be as effective as a legless quadrotor. But if your objective is instead something like inspection or search and rescue, where you need to spend a fair amount of time not moving very much, hopping could be significantly more effective.

Hopcopter is a small quadcopter (specifically a Crazyflie) attached to a springy pogo-stick leg.Songnan Bai, Runze Ding, Song Li, and Bingxuan Pu

Hopcopter can reposition itself on the fly to hop off of different surfaces.Songnan Bai, Runze Ding, Song Li, and Bingxuan Pu

The actual hopping is mostly passive. Hopcopter’s leg is two rigid pieces connected by rubber bands, with a Crazyflie microcopter stapled to the top. During a hop, the Crazyflie can add directional thrust to keep the hops hopping and alter its direction as well as its height, from 0.6 meters to 1.6 meters. There isn’t a lot of room for extra sensors on Hopcopter, but the addition of some stabilizing fins allow for continuous hopping without any positional feedback.

Besides vertical hopping, Hopcopter can also position itself in midair to hop off of surfaces at other orientations, allowing it to almost instantaneously change direction, which is a neat trick.

And it can even do mid air somersaults, because why not?

Hopcopter’s repertoire of tricks includes somersaults.Songnan Bai, Runze Ding, Song Li, and Bingxuan Pu

The researchers, based at the City University of Hong Kong, say that the Hopcopter technology (namely, the elastic leg) could be easily applied to most other quadcopter platforms, turning them into Hopcopters as well. And if you’re more interested in extra payload rather than extra endurance, it’s possible to use hopping in situations where a payload would be too heavy for continuous flight.

The researchers published their work 10 April in Science Robotics.



Last December, the AI Institute announced that it was opening an office in Zurich as a European counterpart to its Boston headquarters and recruited Marco Hutter to helm the office. Hutter also runs the Robotic Systems Lab at ETH Zurich, arguably best known as the origin of the ANYmal quadruped robot (but it also does tons of other cool stuff).

We’re doing our best to keep close tabs on the institute, because it’s one of a vanishingly small number of places that currently exist where roboticists have the kind of long-term resources and vision necessary to make substantial progress on really hard problems that aren’t quite right for either industry or academia. The institute is still scaling up (and the branch in Zurich has only just kicked things off), but we did spot some projects that the Boston folks have been working on, and as you can see from the clips at the top of this page, they’re looking pretty cool.

Meanwhile, we had a chance to check in with Marco Hutter to get a sense of what the Zurich office will be working on and how he’s going to be solving all of the hard problems in robotics. All of them!

How much can you tell us about what you’ll be working on at the AI Institute?

Marco Hutter: If you know the research that I’ve been doing in the past at ETH and with our startups, there’s an overlap on making systems more mobile, making systems more able to interact with the world, making systems in general more capable on the hardware and software side. And that’s what the institute strives for.

The institute describes itself as a research organization that aims to solve the most important and fundamental problems in robotics and AI. What do you think those problems are?

Marco Hutter is the head of the AI Institute’s new Zurich branch.Swiss Robotics Day

Hutter: There are lots of problems. If you’re looking at robots today, we have to admit that they’re still pretty stupid. The way they move, their capability of understanding their environment, the way they’re able to interact with unstructured environments—I think we’re still lacking a lot of skills on the robotic side to make robots useful in all of the tasks we wish them to do. So we have the ambition of having these robots taking over all these dull, dirty, and dangerous jobs. But if we’re honest, today the biggest impact is really only for the dull part. And I think these dirty and dangerous jobs, where we really need support from robots, that’s still going to take a lot of fundamental work on the robotics and AI side to make enough progress for robots to become useful tools.

What is it about the institute that you think will help robotics make more progress in these areas?

Hutter: I think the institute is one of these unique places where we are trying to bring the benefits of the academic world and the benefits from this corporate world together. In academia, we have all kinds of crazy ideas and we try to develop them in all different directions, but at the same time, we have limited engineering support, and we can only go so far. Making robust and reliable hardware systems is a massive effort, and that kind of engineering is much better done in a corporate lab.

You’ve seen this a little bit with the type of work my lab has been doing in the past. We built simple quadrupeds with a little bit of mobility, but in order to make them robust, we eventually had to spin it out. We had to bring it to the corporate world, because for a research group, a pure academic group, it would have been impossible. But at the same time, you’re losing something, right? Once you go into your corporate world and you’re running a business, you have to be very focused; you can’t be that explorative and free anymore.

So if you bring these two things together through the institute, with long-term planning, enough financial support, and brilliant people both in the U.S. and Europe working together, I think that’s what will hopefully help us make significant progress in the next couple of years.

“We’re very different from a traditional company, where at some point you need to have a product that makes money. Here, it’s really about solving problems and taking the next step.” —Marco Hutter, AI Institute

And what will that actually mean in the context of dynamically mobile robots?

Hutter: If you look at Boston Dynamics’ Atlas doing parkour, or ANYmal doing parkour, these are still demonstrations. You don’t see robots running around in the forests or robots working in mines and doing all kinds of crazy maintenance operations, or in industrial facilities, or construction sites, you name it. We need to not only be able to do this once as a prototype demonstration, but to have all the capabilities that bring that together with environmental perception and understanding to make this athletic intelligence more capable and more adaptable to all kinds of different environments. This is not something that from today to tomorrow we’re going to see it being revolutionized—it will be gradual, steady progress because I think there’s still a lot of fundamental work that needs to be done.

I feel like the mobility of legged robots has improved a lot over the last five years or so, and a lot of that progress has come from Boston Dynamics and also from your lab. Do you feel the same?

Hutter: There has always been progress; the question is how much you can zoom in or zoom out. I think one thing has changed quite a bit, and that’s the availability of robotic systems to all kinds of different research groups. If you look back a decade, people had to build their own robots, they had to do the control for the robots, they had to work on the perception for the robots, and putting everything together like that makes it extremely fragile and very challenging to make something that works more than once. That has changed, which allows us to make faster progress.

Marc Raibert (founder of the AI Institute) likes to show videos of mountain goats to illustrate what robots should be (or will be?) capable of. Does that kind of thing inspire you as well?

Hutter: If you look at the animal kingdom, there’s so many things you can draw inspiration from. And a lot of this stuff is not only the cognitive side; it’s really about pairing the cognitive side with the mechanical intelligence of things like the simple-seeming hooves of mountain goats. But they’re really not that simple, they’re pretty complex in how they interact with the environment. Having one of these things and not the other won’t allow the animal to move across its challenging environment. It’s the same thing with the robots.

It’s always been like this in robotics, where you push on the hardware side, and your controls become better, so you hit a hardware limitation. So both things have to evolve hand in hand. Otherwise, you have an over-dimensioned hardware system that you can’t use because you don’t have the right controls, or you have very sophisticated controls and your hardware system can’t keep up.

How do you feel about all of the investment into humanoids right now, when quadrupedal robots with arms have been around for quite a while?

Hutter: There’s a lot of ongoing research on quadrupeds with arms, and the nice thing is that these technologies that are developed for mobile systems with arms are the same technologies that are used in humanoids. It’s not different from a research point of view, it’s just a different form factor for the system. I think from an application point of view, the story from all of these companies making humanoids is that our environment has been adapted to humans quite a bit. A lot of tasks are at the height of a human standing, right? A quadruped doesn’t have the height to see things or to manipulate things on a table. It’s really application dependent, and I wouldn’t say that one system is better than the other.



Rapid and resourceful technological improvisation has long been a mainstay of warfare, but the war in Ukraine is taking it to a new level. This improvisation is most conspicuous in the ceaselessly evolving struggle between weaponized drones and electronic warfare, a cornerstone of this war.

Weaponized civilian first-person-view (FPV) drones began dramatically reshaping the landscape of the war in the summer of 2023. Prior to this revolution, various commercial drones played critical roles, primarily for intelligence, surveillance, and reconnaissance. Since 2014, the main means of defending against these drones has been electronic warfare (EW), in its many forms. The iterative, lethal dance between drones and EW has unfolded a rich technological tapestry, revealing insights into a likely future of warfare where EW and drones intertwine.

After the invasion of Crimea, in 2014, Ukrainian forces depended heavily on commercial off-the-shelf drones, such as models from DJI, for reconnaissance and surveillance. These were not FPV drones, for the most part. Russia’s response involved deploying military-grade EW systems alongside law-enforcement tools like Aeroscope, a product from DJI that allows instant identification and tracking of drones from their radio emissions. Aeroscope, while originally a standard tool used by law enforcement to detect and track illegal drone flights, soon revealed its military potential by pinpointing both the drone and its operator.

On both sides of the line you’ll find much the same kind of people doing much the same thing: hacking.

This application turned a security feature into a significant tactical asset, providing Russian artillery units with precise coordinates for their targets—namely, Ukrainian drone operators. To circumvent this vulnerability, groups of Ukrainian volunteers innovated. By updating the firmware of the DJI drones, they closed the backdoors that allowed the drones to be tracked by Aeroscope. Nevertheless, after the start of the conflict in Crimea, commercial, off-the-shelf drones were considered a last-resort asset used by volunteers to compensate for the lack of proper military systems. To be sure, the impact of civilian drones during this period was not comparable to what occurred after the February 2022 invasion.

As Russia’s “thunder-run” strategy became bogged down shortly after the invasion, Russian forces found themselves unexpectedly vulnerable to civilian drones, in part because most of their full-scale military EW systems were not very mobile.

During a training exercise in southern Ukraine in May 2023, a drone pilot maneuvered a flier to a height of 100 meters before dropping a dummy anti-tank grenade on to a pile of tires. The test, pictured here, worked—that night the pilot’s team repeated the exercise over occupied territory, blowing up a Russian armored vehicle. Emre Caylak/Guardian/eyevine/Redux

The Russians could have compensated by deploying many Aeroscope terminals then, but they didn’t, because most Russian officers at the time had a dismissive view of the capabilities of civilian drones in a high-intensity conflict. That failure opened a window of opportunity that Ukrainian armed-forces units exploited aggressively. Military personnel, assisted by many volunteer technical specialists, gained a decisive intelligence advantage for their forces by quickly fielding fleets of hundreds of camera drones connected to simple yet effective battlefield-management systems. They soon began modifying commercial drones to attack, with grenade tosses and, ultimately, “kamikaze” operations. Besides the DJI models, one of the key drones was the R18, an octocopter developed by the Ukrainian company Aerorozvidka, capable of carrying three grenades or small bombs. As casualties mounted, Russian officers soon realized the extent of the threat posed by these drones.

How Russian electronic warfare evolved to counter the drone threat

By spring 2023, as the front lines stabilized following strategic withdrawals and counteroffensives, it was clear that the nature of drone warfare had evolved. Russian defenses had adapted, deploying more sophisticated counter-drone systems. Russian forces were also beginning to use drones, setting the stage for the nuanced cat-and-mouse game that has been going on ever since.

The modular construction of first-person-view drones allowed for rapid evolution to enhance their resilience against electronic warfare.

For example, early on, most Russian EW efforts primarily focused on jamming the drones’ radio links for control and video. This wasn’t too hard, given that DJI’s OcuSync protocol was not designed to withstand dense jamming environments. So by April 2023, Ukrainian drone units had begun pivoting toward first-person-view (FPV) drones with modular construction, enabling rapid adaptation to, and evasion of, EW countermeasures.

The Russian awakening to the importance of drones coincided with the stabilization of the front lines, around August 2022. Sluggish Russian offensives came at a high cost, with an increasing proportion of casualties caused directly or indirectly by drone operators. By this time, the Ukrainians were hacking commercial drones, such as DJI Mavics, to “anonymize” them, rendering Aeroscope useless. It was also at this time that the Russians began to adopt commercial drones and develop their own tactics, techniques, and procedures, leveraging their EW and artillery advantages while attempting to compensate for their delay in combat-drone usage.

On 4 March, a Ukrainian soldier flew a drone at a testing site near the town of Kreminna in eastern Ukraine. The drone was powered by a blue battery pack and carried a dummy bomb.David Guttenfelder/The New York Times/Redux

Throughout 2023, when the primary EW tactic employed was jamming, the DJI drones began to fall out of favor for attack roles. When the density of Russian jammer usage surpassed a certain threshold, DJI’s OcuSync radio protocol, which controls a drone’s flight direction and video, could not cope with it. Being proprietary, OcuSync’s frequency band and power are not modifiable. A jammer can attack both the control and video signals, and the drone becomes unrecoverable most of the time. As a result, DJI drones have lately been used farther from the front lines and relegated mainly to roles in intelligence, surveillance, and reconnaissance. Meanwhile, the modular construction of FPVs allowed for rapid evolution to enhance their resilience against EW. The Ukraine war greatly boosted the world’s production of FPV drones; at this point there are thousands of FPV models and modifications.

A “kamikaze” first-person-view drone with an attached PG-7L round, intended for use with an RPG-7 grenade launcher, is readied for a mission near the town of Horlivka, in the Donetsk region, on 17 January 2024. The drone was prepared by a Ukrainian serviceman of the Rarog UAV squadron of the 24th Separate Mechanized Brigade.Inna Varenytsia/Reuters/Redux

As of early 2024, analog video signals are the most popular option by far. This technology offers drone operators a brief window of several seconds to correct the drone’s path upon detecting interference, for example as a result of jamming, before signal loss. Additionally, drone manufacturers have access to more powerful video transmitters, up to 5 watts, which are more resistant to jamming. Furthermore, the 1.2-gigahertz frequency band is gaining popularity over the previously dominant 5.8-GHz band due to its superior obstacle penetration and because fewer jammers are targeting that band.

However, the lack of encryption in analog video transmitter systems means that a drone’s visual feed can be intercepted by any receiver. So various mitigation strategies have been explored. These include adding encryption layers and using digital-control and video protocols such as HDZero, Walksnail, or, especially, any of several new open-source alternatives.

In the war zone, the most popular of these open-source control radio protocols is ExpressLRS, or ELRS. Being open-source, ELRS not only offers more affordable hardware than its main rival, TBS Crossfire, it is also modifiable via its software. It has been hacked in order to use frequency bands other than its original 868 to 915 megahertz. This adaptation produces serious headaches for EW operators, because they have to cover a much wider band. As of March 2024, Ukrainian drone operators are performing final tests on 433-MHz ELRS transmitter-receiver pairs, further challenging prevailing EW methods.

Distributed mass in the transparent battlefield

Nevertheless, the most important recent disruption of all in the drone-versus-EW struggle is distributed mass. Instead of an envisioned blitzkrieg-style swarm with big clouds of drones hitting many closely spaced targets during very short bursts, an ever-growing number of drones are covering more widely dispersed targets over a much longer time period, whenever the weather is conducive. Distributed mass is a cornerstone of the emerging transparent battlefield, in which many different sensors and platforms transmit huge amounts of data that is integrated in real time to provide a comprehensive view of the battlefield. One offshoot of this strategy is that more and more kamikaze drones are directed toward a constantly expanding range of targets. Electronic warfare is adapting to this new reality, confronting mass with mass: massive numbers of drones against massive numbers of RF sensors and jammers.

Ukraine is the first true war of the hackers.

Attacks now often consist of far more commercial drones than a suite of RF detectors or jammers could handle even six months ago. With brute-force jamming, even if defenders are willing to accept high rates of damage inflicted on their own offensive drones, these previous EW systems are just not up to the task. So for now, at least, the drone hackers are in the lead in this deadly game of “hacksymmetrical” warfare. Their development cycle is far too rapid for conventional electronic warfare to keep pace.

But the EW forces are not standing still. Both sides are either developing or acquiring civilian RF-detecting equipment, while military-tech startups and even small volunteer groups are developing new, simple, and good-enough jammers in essentially the same improvised ways that hackers would.

Ukrainian soldiers familiarized themselves with a portable drone jammer during a training session in Kharkiv, Ukraine, on 11 March 2024.Diego Herrera Carcedo/Anadolu/Getty Images

Two examples illustrate this trend. Increasingly affordable, short-range jammers are being installed on tanks, armored personnel carriers, trucks, pickups, and even 4x4s. Although limited and unsophisticated, these systems contribute to drone-threat mitigation. In addition, a growing number of soldiers on the front line carry simple, commercial radio-frequency (RF) scanners with them. Configured to detect drones across various frequency bands, these devices, though far from perfect, have begun to save lives by providing precious additional seconds of warning before an imminent drone attack.

The electronic battlefield has now become a massive game of cat and mouse. Because commercial drones have proven so lethal and disruptive, drone operators have become high-priority targets. As a result, operators have had to reinvent camouflage techniques, while the hackers who drive the evolution of their drones are working on every modification of RF equipment that offers an advantage. Besides the frequency-band modification described above, hackers have developed and refined two-way, two-signal repeaters for drones. Such systems are attached to another drone that hovers close to the operator and well above the ground, relaying signals to and from the attacking drone. Such repeaters more than double the practical range of drone communications, and thus the EW “cats” in this game have to search a much wider area than before.

Hackers and an emerging cottage industry of war startups are raising the stakes. Their primary goal is to erode the effectiveness of jammers by attacking them autonomously. In this countermeasure, offensive drones are equipped with home-on-jam systems. Over the next several months, increasingly sophisticated versions of these systems will be fielded. These home-on-jam capabilities will autonomously target any jamming emission within range; this range, which is classified, depends on emission power at a rate that is believed to be 0.3 kilometers per watt. In other words, if a jammer has 100 W of signal power, it can be detected up to 30 km away, and then attacked. After these advances allow the drone “mice” to hunt the EW cat, what will happen to the cat?

The challenge is unprecedented and the outcome uncertain. But on both sides of the line you’ll find much the same kind of people doing much the same thing: hacking. Civilian hackers have for years lent their skills to such shady enterprises as narco-trafficking and organized crime. Now hacking is a major, indispensable component of a full-fledged war, and its practitioners have emerged from a gray zone of plausible deniability into the limelight of military prominence. Ukraine is the first true war of the hackers.

The implications for Western militaries are ominous. We have neither masses of drones nor masses of EW tech. What is worse, the world’s best hackers are completely disconnected from the development of defense systems. The Ukrainian experience, where a vibrant war startup scene is emerging, suggests a model for integrating maverick hackers into our defense strategies. As the first hacker war continues to unfold, it serves as a reminder that in the era of electronic and drone warfare, the most critical assets are not just the technologies we deploy but also the scale and the depth of the human ingenuity behind them.



Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

RoboCup German Open: 17–21 April 2024, KASSEL, GERMANYAUVSI XPONENTIAL 2024: 22–25 April 2024, SAN DIEGOEurobot Open 2024: 8–11 May 2024, LA ROCHE-SUR-YON, FRANCEICRA 2024: 13–17 May 2024, YOKOHAMA, JAPANRoboCup 2024: 17–22 July 2024, EINDHOVEN, NETHERLANDS

Enjoy today’s videos!

USC, UPenn, Texas A&M, Oregon State, Georgia Tech, Temple University, and NASA Johnson Space Center are teaching dog-like robots to navigate craters of the moon and other challenging planetary surfaces in research funded by NASA.

[ USC ]

AMBIDEX is a revolutionary robot that is fast, lightweight, and capable of human-like manipulation. We have added a sensor head and the torso and the waist to greatly expand the range of movement. Compared to the previous arm-centered version, the overall impression and balance has completely changed.

[ Naver Labs ]

It still needs a lot of work, but the six-armed pollinator, Stickbug, can autonomously navigate and pollinate flowers in a greenhouse now.

I think “needs a lot of work” really means “needs a couple more arms.”

[ Paper ]

Experience the future of robotics as UBTECH’s humanoid robot integrates with Baidu’s ERNIE through AppBuilder! Witness robots [that] understand language and autonomously perform tasks like folding clothes and object sorting.

[ UBTECH ]

I know the fins on this robot are for walking underwater rather than on land, but watching it move, I feel like it’s destined to evolve into something a little more terrestrial.

[ Paper ] via [ HERO Lab ]

iRobot has a new Roomba that vacuums and mops—and at $275, it’s a pretty good deal.

Also, if you are a robot vacuum owner, please, please remember to clean the poor thing out from time to time. Here’s how to do it with a Roomba:

[ iRobot ]

The video demonstrates the wave-basin testing of a 43 kg (95 lb) amphibious cycloidal propeller unmanned underwater vehicle (Cyclo-UUV) developed at the Advanced Vertical Flight Laboratory, Texas A&M University. The use of cyclo-propellers allows for 360 degree thrust vectoring for more robust dynamic controllability compared to UUVs with conventional screw propellers.

[ AVFL ]

Sony is still upgrading Aibo with new features, like the ability to listen to your terrible music and dance along.

[ Aibo ]

Operating robots precisely and at high speeds has been a long-standing goal of robotics research. To enable precise and safe dynamic motions, we introduce a four degree-of-freedom (DoF) tendon-driven robot arm. Tendons allow placing the actuation at the base to reduce the robot’s inertia, which we show significantly reduces peak collision forces compared to conventional motor-driven systems. Pairing our robot with pneumatic muscles allows generating high forces and highly accelerated motions, while benefiting from impact resilience through passive compliance.

[ Max Planck Institute ]

Rovers on Mars have previously been caught in loose soils, and turning the wheels dug them deeper, just like a car stuck in sand. To avoid this, Rosalind Franklin has a unique wheel-walking locomotion mode to overcome difficult terrain, as well as autonomous navigation software.

[ ESA ]

Cassie is able to walk on sand, gravel, and rocks inside the Robot Playground at the University of Michigan.

Aww, they stopped before they got to the fun rocks.

[ Paper ] via [ Michigan Robotics ]

Not bad for 2016, right?

[ Namiki Lab ]

MOMO has learned the Bam Yang Gang dance moves with its hand dexterity. :) By analyzing 2D dance videos, we extract detailed hand skeleton data, allowing us to recreate the moves in 3D using a hand model. With this information, MOMO replicates the dance motions with its arm and hand joints.

[ RILAB ] via [ KIMLAB ]

This UPenn GRASP SFI Seminar is from Eric Jang at 1X Technologies, on “Data Engines for Humanoid Robots.”

1X’s mission is to create an abundant supply of physical labor through androids that work alongside humans. I will share some of the progress 1X has been making towards general-purpose mobile manipulation. We have scaled up the number of tasks our androids can do by combining an end-to-end learning strategy with a no-code system to add new robotic capabilities. Our Android Operations team trains their own models on the data they gather themselves, producing an extremely high-quality “farm-to-table” dataset that can be used to learn extremely capable behaviors. I’ll also share an early preview of the progress we’ve been making towards a generalist “World Model” for humanoid robots.

[ UPenn ]

This Microsoft Future Leaders in Robotics and AI Seminar is from Chahat Deep Singh at the University of Maryland, on “Minimal Perception: Enabling Autonomy in Palm-Sized Robots.”

The solution to robot autonomy lies at the intersection of AI, computer vision, computational imaging, and robotics—resulting in minimal robots. This talk explores the challenge of developing a minimal perception framework for tiny robots (less than 6 inches) used in field operations such as space inspections in confined spaces and robot pollination. Furthermore, we will delve into the realm of selective perception, embodied AI, and the future of robot autonomy in the palm of your hands.

[ UMD ]

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