Feed aggregator

UX-led approach for your robotics design and build

UX design differentiates great products from average products, and helps you stand out from the rest. Now, in the age of robotics, your teams may be creating products with no blueprint to bounce off.

Find out why a UX-led approach for the design and build of your products is critical, learn from the masters in getting this right - first time, speed up the time it takes you to get to market, and deliver products users love.

Download UX design - Your Competitive Advantage today.

This paper demonstrates how tactile and proximity sensing can be used to perform automatic mechanical fractures detection (surface cracks). For this purpose, a custom-designed integrated tactile and proximity sensor has been implemented. With the help of fiber optics, the sensor measures the deformation of its body, when interacting with the physical environment, and the distance to the environment's objects. This sensor slides across different surfaces and records data which are then analyzed to detect and classify fractures and other mechanical features. The proposed method implements machine learning techniques (handcrafted features, and state of the art classification algorithms). An average crack detection accuracy of ~94% and width classification accuracy of ~80% is achieved. Kruskal-Wallis results (p < 0.001) indicate statistically significant differences among results obtained when analysing only integrated deformation measurements, only proximity measurements and both deformation and proximity data. A real-time classification method has been implemented for online classification of explored surfaces. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibers might be more suitable for operation in extreme environments (such as nuclear facilities) where radiation may damage electronic components of commonly employed sensing devices, such as standard force sensors based on strain gauges and video cameras.

In shoe sensor

Researchers at Stevens Institute of Technology are leveraging some of the newest mechanical and robotic technologies to help some of our oldest populations stay healthy, active, and independent.

Yi Guo, professor of electrical and computer engineering and director of the Robotics and Automation Laboratory, and Damiano Zanotto, assistant professor of mechanical engineering, and director of the Wearable Robotic Systems Laboratory, are collaborating with Ashley Lytle, assistant professor in Stevens’ College of Arts and Letters, and Ashwini K. Rao of Columbia University Medical Center, to combine an assistive mobile robot companion with wearable in-shoe sensors in a system designed to help elderly individuals maintain the balance and motion they need to thrive.

“Balance and motion can be significant issues for this population, and if elderly people fall and experience an injury, they are less likely to stay fit and exercise,” Guo said. “As a consequence, their level of fitness and performance decreases. Our mobile robot companion can help decrease the chances of falling and contribute to a healthy lifestyle by keeping their walking function at a good level.”

The mobile robots are designed to lead walking sessions and using the in-shoe sensors, monitor the user’s gait, indicate issues, and adjust the exercise speed and pace. The initiative is part of a four-year National Science Foundation research project.

“For the first time, we’re integrating our wearable sensing technology with an autonomous mobile robot,” said Zanotto, who worked with elderly people at Columbia University Medical Center for three years before coming to Stevens in 2016. “It’s exciting to be combining these different areas of expertise to leverage the strong points of wearable sensing technology, such as accurately capturing human movement, with the advantages of mobile robotics, such as much larger computational powers.”

The team is developing algorithms that fuse real-time data from smart, unobtrusive, in-shoe sensors and advanced on-board sensors to inform the robot’s navigation protocols and control the way the robot interacts with elderly individuals. It’s a promising way to assist seniors in safely doing walking exercises and maintaining their quality of life.

Bringing the benefits of the lab to life

Guo and Zanotto are working with Lytle, an expert in social and health psychology, to implement a social connectivity capability and make the bi-directional interaction between human and robot even more intuitive, engaging, and meaningful for seniors.

“Especially during COVID, it’s important for elderly people living on their own to connect socially with family and friends,” Zanotto said, “and the robot companion will also offer teleconferencing tools to provide that interaction in an intuitive and transparent way.”

“We want to use the robot for social connectedness, perhaps integrating it with a conversation agent such as Alexa,” Guo added. “The goal is to make it a companion robot that can sense, for example, that you are cooking, or you’re in the living room, and help with things you would do there.”

It’s a powerful example of how abstract concepts can have meaningful real-life benefits.

“As engineers, we tend to work in the lab, trying to optimize our algorithms and devices and technologies,” Zanotto noted, “but at the end of the day, what we do has limited value unless it has impact on real life. It’s fascinating to see how the devices and technologies we’re developing in the lab can be applied to make a difference for real people.”

Maintaining balance in a global pandemic

Although COVID-19 has delayed the planned testing at a senior center in New York City, it has not stopped the team’s progress.

“Although we can’t test on elderly populations yet, our students are still testing in the lab,” Guo said. “This summer and fall, for the first time, the students validated the system’s real-time ability to monitor and assess the dynamic margin of stability during walking—in other words, to evaluate whether the person following the robot is walking normally or has a risk of falling. They’re also designing parameters for the robot to give early warnings and feedback that help the human subjects correct posture and gait issues while walking.”

Those warnings would be literally underfoot, as the in-shoe sensors would pulse like a vibrating cell phone to deliver immediate directional information to the subject.

“We’re not the first to use this vibrotactile stimuli technology, but this application is new,” Zanotto said.

So far, the team has published papers in top robotics publication venues including IEEE Transactions on Neural Systems and Rehabilitation Engineering and the 2020 IEEE International Conference on Robotics and Automation (ICRA). It’s a big step toward realizing the synergies of bringing the technical expertise of engineers to bear on the clinical focus on biometrics—and the real lives of seniors everywhere.

For naive robots to become truly autonomous, they need a means of developing their perceptive capabilities instead of relying on hand crafted models. The sensorimotor contingency theory asserts that such a way resides in learning invariants of the sensorimotor flow. We propose a formal framework inspired by this theory for the description of sensorimotor experiences of a naive agent, extending previous related works. We then use said formalism to conduct a theoretical study where we isolate sufficient conditions for the determination of a sensory prediction function. Furthermore, we also show that algebraic structure found in this prediction can be taken as a proxy for structure on the motor displacements, allowing for the discovery of the combinatorial structure of said displacements. Both these claims are further illustrated in simulations where a toy naive agent determines the sensory predictions of its spatial displacements from its uninterpreted sensory flow, which it then uses to infer the combinatorics of said displacements.

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

ACRA 2020 – December 8-10, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

“Who doesn’t love giant robots?”

Luma, is a towering 8 metre snail which transforms spaces with its otherworldly presence. Another piece, Triffid, stands at 6 metres and its flexible end sweeps high over audiences’ heads like an enchanted plant. The movement of the creatures is inspired by the flexible, wiggling and contorting motions of the animal kingdom and is designed to provoke instinctive reactions and emotions from the people that meet them. Air Giants is a new creative robotic studio founded in 2020. They are based in Bristol, UK, and comprise a small team of artists, roboticists and software engineers. The studio is passionate about creating emotionally effective motion at a scale which is thought-provoking and transporting, as well as expanding the notion of what large robots can be used for.

Here’s a behind the scenes and more on how the creatures work.

[ Air Giants ]

Thanks Emma!

If the idea of submerging a very expensive sensor payload being submerged in a lake makes you as uncomfortable as it makes me, this is not the video for you.

[ ANYbotics ]

As the pandemic continues on, the measures due to this health crisis are increasingly stringent, and working from home continues to be promoted and solicited by many companies, Pepper will allow you to keep in touch with your relatives or even your colleagues.

[ Softbank ]

Fairly impressive footwork from Tencent Robotics.

Although, LittleDog was doing that like a decade ago:

[ Tencent ]

It's been long enough since I've been able to go out for boba tea that a robotic boba tea kiosk seems like a reasonable thing to get for my living room.

[ Bobacino ] via [ Gizmodo ]

Road construction and maintenance is challenging and dangerous work. Pioneer Industrial Systems has spent over twenty years designing custom robotic systems for industrial manufacturers around the world. These robotic systems greatly improve safety and increase efficiency. Now they’re taking that expertise on the road, with the Robotic Maintenance Vehicle. This base unit can be mounted on a truck or trailer, and utilizes various modules to perform a variety of road maintenance tasks.

[ Pioneer ]

Extend Robotics arm uses cloud-based teleoperation software, featuring human-like dexterity and intelligence, with multiple applications in healthcare, utilities and energy

[ Extend Robotics ]

ARC, short for “AI, Robot, Cloud,” includes the latest algorithms and high precision data required for human-robot coexistence. Now with ultra-low latency networks, many robots can simultaneously become smarter, just by connecting to ARC. “ARC Eye” serves as the eyes for all robots, accurately determining the current location and route even indoors where there is no GPS access. “ARC Brain” is the computing system shared simultaneously by all robots, which plans and processes movement, localization, and task performance for the robot.

[ Naver Labs ]

How can we re-imagine urban infrastructures with cutting-edge technologies? Listen to this webinar from Ger Baron, Amsterdam’s CTO, and Senseable City Lab’s researchers, on how MIT and Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) are reimagining Amsterdam’s canals with the first fleet of autonomous boats.

[ MIT ]

Join Guy Burroughes in this webinar recording to hear about Spot, the robot dog created by Boston Dynamics, and how RACE plan to use it in nuclear decommissioning and beyond.

[ UKAEA ]

This GRASP on Robotics seminar comes from Marco Pavone at Stanford University, "On Safe and Efficient Human-robot interactions via Multimodal Intent Modeling and Reachability-based Safety Assurance."

In this talk I will present a decision-making and control stack for human-robot interactions by using autonomous driving as a motivating example. Specifically, I will first discuss a data-driven approach for learning multimodal interaction dynamics between robot-driven and human-driven vehicles based on recent advances in deep generative modeling. Then, I will discuss how to incorporate such a learned interaction model into a real-time, interaction-aware decision-making framework. The framework is designed to be minimally interventional; in particular, by leveraging backward reachability analysis, it ensures safety even when other cars defy the robot's expectations without unduly sacrificing performance. I will present recent results from experiments on a full-scale steer-by-wire platform, validating the framework and providing practical insights. I will conclude the talk by providing an overview of related efforts from my group on infusing safety assurances in robot autonomy stacks equipped with learning-based components, with an emphasis on adding structure within robot learning via control-theoretical and formal methods.

[ UPenn ]

Autonomous Systems Failures: Who is Legally and Morally Responsible? Sponsored by Northwestern University’s Law and Technology Initiative and AI@NU, the event was moderated by Dan Linna and included Northwestern Engineering's Todd Murphey, University of Washington Law Professor Ryan Calo, and Google Senior Research Scientist Madeleine Clare Elish.

[ Northwestern ]

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

ACRA 2020 – December 8-10, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

“Who doesn’t love giant robots?”

Luma, is a towering 8 metre snail which transforms spaces with its otherworldly presence. Another piece, Triffid, stands at 6 metres and its flexible end sweeps high over audiences’ heads like an enchanted plant. The movement of the creatures is inspired by the flexible, wiggling and contorting motions of the animal kingdom and is designed to provoke instinctive reactions and emotions from the people that meet them. Air Giants is a new creative robotic studio founded in 2020. They are based in Bristol, UK, and comprise a small team of artists, roboticists and software engineers. The studio is passionate about creating emotionally effective motion at a scale which is thought-provoking and transporting, as well as expanding the notion of what large robots can be used for.

Here’s a behind the scenes and more on how the creatures work.

[ Air Giants ]

Thanks Emma!

If the idea of submerging a very expensive sensor payload being submerged in a lake makes you as uncomfortable as it makes me, this is not the video for you.

[ ANYbotics ]

As the pandemic continues on, the measures due to this health crisis are increasingly stringent, and working from home continues to be promoted and solicited by many companies, Pepper will allow you to keep in touch with your relatives or even your colleagues.

[ Softbank ]

Fairly impressive footwork from Tencent Robotics.

Although, LittleDog was doing that like a decade ago:

[ Tencent ]

It's been long enough since I've been able to go out for boba tea that a robotic boba tea kiosk seems like a reasonable thing to get for my living room.

[ Bobacino ] via [ Gizmodo ]

Road construction and maintenance is challenging and dangerous work. Pioneer Industrial Systems has spent over twenty years designing custom robotic systems for industrial manufacturers around the world. These robotic systems greatly improve safety and increase efficiency. Now they’re taking that expertise on the road, with the Robotic Maintenance Vehicle. This base unit can be mounted on a truck or trailer, and utilizes various modules to perform a variety of road maintenance tasks.

[ Pioneer ]

Extend Robotics arm uses cloud-based teleoperation software, featuring human-like dexterity and intelligence, with multiple applications in healthcare, utilities and energy

[ Extend Robotics ]

ARC, short for “AI, Robot, Cloud,” includes the latest algorithms and high precision data required for human-robot coexistence. Now with ultra-low latency networks, many robots can simultaneously become smarter, just by connecting to ARC. “ARC Eye” serves as the eyes for all robots, accurately determining the current location and route even indoors where there is no GPS access. “ARC Brain” is the computing system shared simultaneously by all robots, which plans and processes movement, localization, and task performance for the robot.

[ Naver Labs ]

How can we re-imagine urban infrastructures with cutting-edge technologies? Listen to this webinar from Ger Baron, Amsterdam’s CTO, and Senseable City Lab’s researchers, on how MIT and Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) are reimagining Amsterdam’s canals with the first fleet of autonomous boats.

[ MIT ]

Join Guy Burroughes in this webinar recording to hear about Spot, the robot dog created by Boston Dynamics, and how RACE plan to use it in nuclear decommissioning and beyond.

[ UKAEA ]

This GRASP on Robotics seminar comes from Marco Pavone at Stanford University, "On Safe and Efficient Human-robot interactions via Multimodal Intent Modeling and Reachability-based Safety Assurance."

In this talk I will present a decision-making and control stack for human-robot interactions by using autonomous driving as a motivating example. Specifically, I will first discuss a data-driven approach for learning multimodal interaction dynamics between robot-driven and human-driven vehicles based on recent advances in deep generative modeling. Then, I will discuss how to incorporate such a learned interaction model into a real-time, interaction-aware decision-making framework. The framework is designed to be minimally interventional; in particular, by leveraging backward reachability analysis, it ensures safety even when other cars defy the robot's expectations without unduly sacrificing performance. I will present recent results from experiments on a full-scale steer-by-wire platform, validating the framework and providing practical insights. I will conclude the talk by providing an overview of related efforts from my group on infusing safety assurances in robot autonomy stacks equipped with learning-based components, with an emphasis on adding structure within robot learning via control-theoretical and formal methods.

[ UPenn ]

Autonomous Systems Failures: Who is Legally and Morally Responsible? Sponsored by Northwestern University’s Law and Technology Initiative and AI@NU, the event was moderated by Dan Linna and included Northwestern Engineering's Todd Murphey, University of Washington Law Professor Ryan Calo, and Google Senior Research Scientist Madeleine Clare Elish.

[ Northwestern ]

End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

COVID-19 can induce severe respiratory problems that need prolonged mechanical ventilation in the intensive care unit. While Open Tracheostomy (OT) is the preferred technique due to the excellent visualization of the surgical field and structures, Percutaneous Tracheostomy (PT) has proven to be a feasible minimally invasive alternative. However, PT's limitation relates to the inability to precisely enter the cervical trachea at the exact spot since the puncture is often performed based on crude estimation from anatomical laryngeal surface landmarks. Besides, there is no absolute control of the trajectory and force required to make the percutaneous puncture into the trachea, resulting in inadvertent injury to the cricoid ring, cervical esophagus, and vessels in the neck. Therefore, we hypothesize that a flexible mini-robotic system, incorporating the robotic needling technology, can overcome these challenges by allowing the trans-oral robotic instrument of the cervical trachea. This approach promises to improve current PT technology by making the initial trachea puncture from an “inside-out” approach, rather than an “outside-in” manner, fraught with several technical uncertainties.

Current designs of powered prosthetic limbs are limited by the nearly exclusive use of DC motor technology. Soft actuators promise new design freedom to create prosthetic limbs which more closely mimic intact neuromuscular systems and improve the capabilities of prosthetic users. This work evaluates the performance of a hydraulically amplified self-healing electrostatic (HASEL) soft actuator for use in a prosthetic hand. We compare a linearly-contracting HASEL actuator, termed a Peano-HASEL, to an existing actuator (DC motor) when driving a prosthetic finger like those utilized in multi-functional prosthetic hands. A kinematic model of the prosthetic finger is developed and validated, and is used to customize a prosthetic finger that is tuned to complement the force-strain characteristics of the Peano-HASEL actuators. An analytical model is used to inform the design of an improved Peano-HASEL actuator with the goal of increasing the fingertip pinch force of the prosthetic finger. When compared to a weight-matched DC motor actuator, the Peano-HASEL and custom finger is 10.6 times faster, has 11.1 times higher bandwidth, and consumes 8.7 times less electrical energy to grasp. It reaches 91% of the maximum range of motion of the original finger. However, the DC motor actuator produces 10 times the fingertip force at a relevant grip position. In this body of work, we present ways to further increase the force output of the Peano-HASEL driven prosthetic finger system, and discuss the significance of the unique properties of Peano-HASELs when applied to the field of upper-limb prosthetic design. This approach toward clinically-relevant actuator performance paired with a substantially different form-factor compared to DC motors presents new opportunities to advance the field of prosthetic limb design.

Forests present one of the most challenging environments for computer vision due to traits, such as complex texture, rapidly changing lighting, and high dynamicity. Loop closure by place recognition is a crucial part of successfully deploying robotic systems to map forests for the purpose of automating conservation. Modern CNN-based place recognition systems like NetVLAD have reported promising results, but the datasets used to train and test them are primarily of urban scenes. In this paper, we investigate how well NetVLAD generalizes to forest environments and find that it out performs state of the art loop closure approaches. Finally, integrating NetVLAD with ORBSLAM2 and evaluating on a novel forest data set, we find that, although suitable locations for loop closure can be identified, the SLAM system is unable to resolve matched places with feature correspondences. We discuss additional considerations to be addressed in future to deal with this challenging problem.

To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach. In particular, some recent meta-learning methods are shown to solve novel tasks by leveraging their experience of performing other tasks during training. Although studies around meta-learning of robot control have worked on improving the performance, the safety issue has not been fully explored, which is also an important consideration in the deployment. In this paper, we firstly relate uncertainty on task inference with the safety in meta-learning of visual imitation, and then propose a novel framework for estimating the task uncertainty through probabilistic inference in the task-embedding space, called PETNet. We validate PETNet with a manipulation task with a simulated robot arm in terms of the task performance and uncertainty evaluation on task inference. Following the standard benchmark procedure in meta-imitation learning, we show PETNet can achieve the same or higher level of performance (success rate of novel tasks at meta-test time) as previous methods. In addition, by testing PETNet with semantically inappropriate or synthesized out-of-distribution demonstrations, PETNet shows the ability to capture the uncertainty about the tasks inherent in the given demonstrations, which allows the robot to identify situations where the controller might not perform properly. These results illustrate our proposal takes a significant step forward to the safe deployment of robot learning systems into diverse tasks and environments.

The use of a robotic arm manipulator as a platform for coincident radiation mapping and laser profiling of radioactive sources on a flat surface is investigated in this work. A combined scanning head, integrating a micro-gamma spectrometer and Time of Flight (ToF) sensor were moved in a raster scan pattern across the surface, autonomously undertaken by the robot arm over a 600 × 260 mm survey area. A series of radioactive sources of different emission intensities were scanned in different configurations to test the accuracy and sensitivity of the system. We demonstrate that in each test configuration the system was able to generate a centimeter accurate 3D model complete with an overlaid radiation map detailing the emitted radiation intensity and the corrected surface dose rate.

In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.

The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.

Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.

This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:

Image: University of Bristol Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.

So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points. 

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.

We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.

There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.

The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.”

In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.

The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.

Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.

This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:

Image: University of Bristol Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.

So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points. 

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.

We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.

There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.

The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.”

Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

IROS 2020 – October 25-25, 2020 – [Online] Bay Area Robotics Symposium – November 20, 2020 – [Online] ACRA 2020 – December 8-10, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

Sixteen teams chose their roster of virtual robots and sensor payloads, some based on real-life subterranean robots, and submitted autonomy and mapping algorithms that SubT Challenge officials then tested across eight cave courses in the cloud-based SubT Simulator. Their robots traversed the cave environments autonomously, without any input or adjustments from human operators. The Cave Circuit Virtual Competition teams earned points by correctly finding, identifying, and localizing up to 20 artifacts hidden in the cave courses within five-meter accuracy.

[ SubT ]

This year, the KUKA Innovation Award’s international jury of experts received a total of more than 40 ideas. The five finalist teams had time until November to implement their ideas. A KUKA LBR Med lightweight robot – the first robotic component to be certified for integration into a medical device – has been made available to them for this purpose. Beyond this, the teams have received a training for the hardware and coaching from KUKA experts throughout the competition. At virtual.MEDICA from 16-19.11.2020, the finalists presented their concepts to an international audience of experts and to the Innovation Award jury. 

The winner of the KUKA Innovation Award 2020, worth 20,000 euros, is Team HIFUSK from the Scuola Superiore Sant'Anna in Italy.

KUKA Innovation Award ]

Like everything else the in-person Cybathlon event was cancelled, but the competition itself took place, just a little more distributed than it would have been otherwise.

[ Cybathlon ]

Matternet, developer of the world's leading urban drone logistics platform, today announced the launch of operations at Labor Berlin Charité Vivantes in Germany. The program kicked-off November 17, 2020 with permanent operations expected to take flight next year, creating the first urban BVLOS [Beyond Visual Line of Sight] medical drone delivery network in the European Union. The drone network expects to significantly improve the timeliness and efficiency of Labor Berlin’s diagnostics services by providing an option to avoid roadway delays, which will improve patient experience with potentially life-saving benefits and lower costs.

Routine BVLOS over an urban area? Impressive.

[ Matternet ]

Robots playing diabolo!

Thanks Thilo!

[ OMRON Sinic X]

Anki's tech has been repackaged into this robot that serves butter:

[ Butter Robot ]

Berkshire Grey just announced our Picking With Purpose Program in which we’ve partnered our robotic automation solutions with food rescue organizations City Harvest and The Greater Boston Food Bank to pick, pack, and distribute food to families in need in time for Thanksgiving. Berkshire Grey donated about 40,000 pounds of food, used one of our robotic automation systems to pick and pack that food into meal boxes for families in need, and our team members volunteered to run the system. City Harvest and The Greater Boston Food Bank are distributing the 4,000 meal boxes we produced. This is just the beginning. We are building a sponsorship program to make Picking With Purpose an ongoing initiative.

[ Berkshire Grey ]

Thanks Peter!

We posted a video previously of Cassie learning to skip, but here's a much more detailed look (accompanying an ICRA submission) that includes some very impressive stair descending.

[ DRL ]

From garage inventors to university students and entrepreneurs, NASA is looking for ideas on how to excavate the Moon’s icy regolith, or dirt, and deliver it to a hypothetical processing plant at the lunar South Pole. The NASA Break the Ice Lunar Challenge, a NASA Centennial Challenge, is now open for registration. The competition will take place over two phases and will reward new ideas and approaches for a system architecture capable of excavating and moving icy regolith and water on the lunar surface.

[ NASA ]

Adaptation to various scene configurations and object properties, stability and dexterity in robotic grasping manipulation is far from explored. This work presents an origami-based shape morphing fingertip design to actively tackle the grasping stability and dexterity problems. The proposed fingertip utilizes origami as its skeleton providing degrees of freedom at desired positions and motor-driven four-bar-linkages as its transmission components to achieve a compact size of the fingertip.

[ Paper ]

"If Roboy crashes... you die."

[ Roboy ]

Traditionally lunar landers, as well as other large space exploration vehicles, are powered by solar arrays or small nuclear reactors. Rovers and small robots, however, are not big enough to carry their own dedicated power supplies and must be tethered to their larger counterparts via electrical cables. Tethering severely restricts mobility, and cables are prone to failure due to lunar dust (regolith) interfering with electrical contact points. Additionally, as robots become smaller and more complex, they are fitted with additional sensors that require more power, further exacerbating the problem. Lastly, solar arrays are not viable for charging during the lunar night. WiBotic is developing rapid charging systems and energy monitoring base stations for lunar robots, including the CubeRover – a shoebox-sized robot designed by Astrobotic – that will operate autonomously and charge wirelessly on the Moon.

[ WiBotic ]

Watching pick and place robots is my therapy.

[ Soft Robotics ]

It's really, really hard to beat liquid fuel for energy storage, as Quaternium demonstrates with their hybrid drone.

[ Quaternium ]

Thanks Gregorio!

State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a novel quadrotor simulator: Flightmare.

[ Flightmare ]

Drones that chuck fire-fighting balls into burning buildings, sure!

[ LARICS ]

If you missed ROS World, that's okay, because all of the talks are now online. Here's the opening keynote from Vivian Chu and Diligent robotics, along with a couple fun lightning talks.

[ ROS World 2020 ]

This week's CMU RI Seminar is by Chelsea Finn from Stanford University, on Data Scalability for Robot Learning.

Recent progress in robot learning has demonstrated how robots can acquire complex manipulation skills from perceptual inputs through trial and error, particularly with the use of deep neural networks. Despite these successes, the generalization and versatility of robots across environment conditions, tasks, and objects remains a major challenge. And, unfortunately, our existing algorithms and training set-ups are not prepared to tackle such challenges, which demand large and diverse sets of tasks and experiences. In this talk, I will discuss two central challenges that pertain to data scalability: first, acquiring large datasets of diverse and useful interactions with the world, and second, developing algorithms that can learn from such datasets. Then, I will describe multiple approaches that we might take to rethink our algorithms and data pipelines to serve these goals. This will include algorithms that allow a real robot to explore its environment in a targeted manner with minimal supervision, approaches that can perform robot reinforcement learning with videos of human trial-and-error experience, and visual model-based RL approaches that are not bottlenecked by their capacity to model everything about the world.

[ CMU RI ]

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

IROS 2020 – October 25-25, 2020 – [Online] Bay Area Robotics Symposium – November 20, 2020 – [Online] ACRA 2020 – December 8-10, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

Sixteen teams chose their roster of virtual robots and sensor payloads, some based on real-life subterranean robots, and submitted autonomy and mapping algorithms that SubT Challenge officials then tested across eight cave courses in the cloud-based SubT Simulator. Their robots traversed the cave environments autonomously, without any input or adjustments from human operators. The Cave Circuit Virtual Competition teams earned points by correctly finding, identifying, and localizing up to 20 artifacts hidden in the cave courses within five-meter accuracy.

[ SubT ]

This year, the KUKA Innovation Award’s international jury of experts received a total of more than 40 ideas. The five finalist teams had time until November to implement their ideas. A KUKA LBR Med lightweight robot – the first robotic component to be certified for integration into a medical device – has been made available to them for this purpose. Beyond this, the teams have received a training for the hardware and coaching from KUKA experts throughout the competition. At virtual.MEDICA from 16-19.11.2020, the finalists presented their concepts to an international audience of experts and to the Innovation Award jury. 

The winner of the KUKA Innovation Award 2020, worth 20,000 euros, is Team HIFUSK from the Scuola Superiore Sant'Anna in Italy.

KUKA Innovation Award ]

Like everything else the in-person Cybathlon event was cancelled, but the competition itself took place, just a little more distributed than it would have been otherwise.

[ Cybathlon ]

Matternet, developer of the world's leading urban drone logistics platform, today announced the launch of operations at Labor Berlin Charité Vivantes in Germany. The program kicked-off November 17, 2020 with permanent operations expected to take flight next year, creating the first urban BVLOS [Beyond Visual Line of Sight] medical drone delivery network in the European Union. The drone network expects to significantly improve the timeliness and efficiency of Labor Berlin’s diagnostics services by providing an option to avoid roadway delays, which will improve patient experience with potentially life-saving benefits and lower costs.

Routine BVLOS over an urban area? Impressive.

[ Matternet ]

Robots playing diabolo!

Thanks Thilo!

[ OMRON Sinic X]

Anki's tech has been repackaged into this robot that serves butter:

[ Butter Robot ]

Berkshire Grey just announced our Picking With Purpose Program in which we’ve partnered our robotic automation solutions with food rescue organizations City Harvest and The Greater Boston Food Bank to pick, pack, and distribute food to families in need in time for Thanksgiving. Berkshire Grey donated about 40,000 pounds of food, used one of our robotic automation systems to pick and pack that food into meal boxes for families in need, and our team members volunteered to run the system. City Harvest and The Greater Boston Food Bank are distributing the 4,000 meal boxes we produced. This is just the beginning. We are building a sponsorship program to make Picking With Purpose an ongoing initiative.

[ Berkshire Grey ]

Thanks Peter!

We posted a video previously of Cassie learning to skip, but here's a much more detailed look (accompanying an ICRA submission) that includes some very impressive stair descending.

[ DRL ]

From garage inventors to university students and entrepreneurs, NASA is looking for ideas on how to excavate the Moon’s icy regolith, or dirt, and deliver it to a hypothetical processing plant at the lunar South Pole. The NASA Break the Ice Lunar Challenge, a NASA Centennial Challenge, is now open for registration. The competition will take place over two phases and will reward new ideas and approaches for a system architecture capable of excavating and moving icy regolith and water on the lunar surface.

[ NASA ]

Adaptation to various scene configurations and object properties, stability and dexterity in robotic grasping manipulation is far from explored. This work presents an origami-based shape morphing fingertip design to actively tackle the grasping stability and dexterity problems. The proposed fingertip utilizes origami as its skeleton providing degrees of freedom at desired positions and motor-driven four-bar-linkages as its transmission components to achieve a compact size of the fingertip.

[ Paper ]

"If Roboy crashes... you die."

[ Roboy ]

Traditionally lunar landers, as well as other large space exploration vehicles, are powered by solar arrays or small nuclear reactors. Rovers and small robots, however, are not big enough to carry their own dedicated power supplies and must be tethered to their larger counterparts via electrical cables. Tethering severely restricts mobility, and cables are prone to failure due to lunar dust (regolith) interfering with electrical contact points. Additionally, as robots become smaller and more complex, they are fitted with additional sensors that require more power, further exacerbating the problem. Lastly, solar arrays are not viable for charging during the lunar night. WiBotic is developing rapid charging systems and energy monitoring base stations for lunar robots, including the CubeRover – a shoebox-sized robot designed by Astrobotic – that will operate autonomously and charge wirelessly on the Moon.

[ WiBotic ]

Watching pick and place robots is my therapy.

[ Soft Robotics ]

It's really, really hard to beat liquid fuel for energy storage, as Quaternium demonstrates with their hybrid drone.

[ Quaternium ]

Thanks Gregorio!

State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a novel quadrotor simulator: Flightmare.

[ Flightmare ]

Drones that chuck fire-fighting balls into burning buildings, sure!

[ LARICS ]

If you missed ROS World, that's okay, because all of the talks are now online. Here's the opening keynote from Vivian Chu and Diligent robotics, along with a couple fun lightning talks.

[ ROS World 2020 ]

This week's CMU RI Seminar is by Chelsea Finn from Stanford University, on Data Scalability for Robot Learning.

Recent progress in robot learning has demonstrated how robots can acquire complex manipulation skills from perceptual inputs through trial and error, particularly with the use of deep neural networks. Despite these successes, the generalization and versatility of robots across environment conditions, tasks, and objects remains a major challenge. And, unfortunately, our existing algorithms and training set-ups are not prepared to tackle such challenges, which demand large and diverse sets of tasks and experiences. In this talk, I will discuss two central challenges that pertain to data scalability: first, acquiring large datasets of diverse and useful interactions with the world, and second, developing algorithms that can learn from such datasets. Then, I will describe multiple approaches that we might take to rethink our algorithms and data pipelines to serve these goals. This will include algorithms that allow a real robot to explore its environment in a targeted manner with minimal supervision, approaches that can perform robot reinforcement learning with videos of human trial-and-error experience, and visual model-based RL approaches that are not bottlenecked by their capacity to model everything about the world.

[ CMU RI ]

I have a confession to make: A robot haunts my nightmares. For me, Boston Dynamics’ Spot robot is 32.5 kilograms (71.1 pounds) of pure terror. It can climb stairs. It can open doors. Seeing it in a video cannot prepare you for the moment you cross paths on a trade-show floor. Now that companies can buy a Spot robot for US $74,500, you might encounter Spot anywhere.

Spot robots now patrol public parks in Singapore to enforce social distancing during the pandemic. They meet with COVID-19 patients at Boston’s Brigham and Women’s Hospital so that doctors can conduct remote consultations. Imagine coming across Spot while walking in the park or returning to your car in a parking garage. Wouldn’t you want to know why this hunk of metal is there and who’s operating it? Or at least whom to call to report a malfunction?

Robots are becoming more prominent in daily life, which is why I think governments need to create national registries of robots. Such a registry would let citizens and law enforcement look up the owner of any roaming robot, as well as learn that robot’s purpose. It’s not a far-fetched idea: The U.S. Federal Aviation Administration already has a registry for drones.

Governments could create national databases that require any companies operating robots in public spaces to report the robot make and model, its purpose, and whom to contact if the robot breaks down or causes problems. To allow anyone to use the database, all public robots would have an easily identifiable marker or model number on their bodies. Think of it as a license plate or pet microchip, but for bots.

There are some smaller-scale registries today. San Jose’s Department of Transportation (SJDOT), for example, is working with Kiwibot, a delivery robot manufacturer, to get real-time data from the robots as they roam the city’s streets. The Kiwibots report their location to SJDOT using the open-source Mobility Data Specification, which was originally developed by Los Angeles to track Bird scooters.

Real-time location reporting makes sense for Kiwibots and Spots wandering the streets, but it’s probably overkill for bots confined to cleaning floors or patrolling parking lots. That said, any robots that come in contact with the general public should clearly provide basic credentials and a way to hold their operators accountable. Given that many robots use cameras, people may also be interested in looking up who’s collecting and using that data.

I starting thinking about robot registries after Spot became available in June for anyone to purchase. The idea gained specificity after listening to Andra Keay, founder and managing director at Silicon Valley Robotics, discuss her five rules of ethical robotics at an Arm event in October. I had already been thinking that we needed some way to track robots, but her suggestion to tie robot license plates to a formal registry made me realize that people also need a way to clearly identify individual robots.

Keay pointed out that in addition to sating public curiosity and keeping an eye on robots that could cause harm, a registry could also track robots that have been hacked. For example, robots at risk of being hacked and running amok could be required to report their movements to a database, even if they’re typically restricted to a grocery store or warehouse. While we’re at it, Spot robots should be required to have sirens, because there’s no way I want one of those sneaking up on me.

This article appears in the December 2020 print issue as “Who’s Behind That Robot?”

DARPA held the Virtual Cave Circuit event of the Subterranean Challenge on Tuesday in the form of a several hour-long livestream. We got to watch (along with all of the competing teams) as virtual robots explored virtual caves fully autonomously, dodging rockfalls, spotting artifacts, scoring points, and sometimes running into stuff and falling over.

Expert commentary was provided by DARPA, and we were able to watch multiple teams running at once, skipping from highlight to highlight. It was really very well done (you can watch an archive of the entire stream here), but they made us wait until the very end to learn who won: First place went to Coordinated Robotics, with BARCS taking second, and third place going to newcomer Team Dynamo.

Huge congratulations to Coordinated Robotics! It’s worth pointing out that the top three teams were separated by an incredibly small handful of points, and on a slightly different day, with slightly different artifact positions, any of them could have come out on top. This doesn’t diminish Coordinated Robotics’ victory in the least—it means that the competition was fierce, and that the problem of autonomous cave exploration with robots has been solved (virtually, at least) in several different but effective ways.

We know Coordinated Robotics pretty well at this point, but here’s an introduction video:

You heard that right—Coordinated Robotics is just Kevin Knoedler, all by himself. This would be astonishing, if we weren’t already familiar with Kevin’s abilities: He won NASA’s virtual Space Robotics Challenge by himself in 2017, and Coordinated Robotics placed first in the DARPA SubT Virtual Tunnel Circuit and second in the Virtual Urban Circuit. We asked Kevin how he managed to do so spectacularly well (again), and here’s what he told us:

IEEE Spectrum: Can you describe what it was like to watch your team of robots on the live stream, and to see them score the most points?

Kevin Knoedler: It was exciting and stressful watching the live stream. It was exciting as the top few scores were quite close for the cave circuit. It was stressful because I started out behind and worked my way up, but did not do well on the final world. Luckily, not doing well on the first and last worlds was offset by better scores on many of the runs in between. DARPA did a very nice job with their live stream of the cave circuit results.

How did you decide on the makeup of your team, and on what sensors to use?

To decide on the makeup of the team I experimented with quite a few different vehicles. I had a lot of trouble with the X2 and other small ground vehicles flipping over. Based on that I looked at the larger ground vehicles that also had a sensor capable of identifying drop-offs. The vehicles that met those criteria for me were the Marble HD2, Marble Husky, Ozbot ATR, and the Absolem. Of those ground vehicles I went with the Marble HD2. It had a downward looking depth camera that I could use to detect drop-offs and was much more stable on the varied terrain than the X2. I had used the X3 aerial vehicle before and so that was my first choice for an aerial platform. 

What were some things that you learned in Tunnel and Urban that you were able to incorporate into your strategy for Cave?

In the Tunnel circuit I had learned a strategy to use ground vehicles and in the Urban circuit I had learned a strategy to use aerial vehicles. At a high level that was the biggest thing I learned from the previous circuits that I was able to apply to the Cave circuit. At a lower level I was able to apply many of the development and testing strategies from the previous circuits to the Cave circuit.

What aspect of the cave environment was most challenging for your robots?

I would say it wasn't just one aspect of the cave environment that was challenging for the robots. There were quite a few challenging aspects of the cave environment. For the ground vehicles there were frequently paths that looked good as the robot started on the path, but turned into drop-offs or difficult boulder crawls. While it was fun to see the robot plan well enough to slowly execute paths over the boulders, I was wishing that the robot was smart enough to try a different path rather than wasting so much time crawling over the large boulders. For the aerial vehicles the combination of tight paths along with large vertical spaces was the biggest challenge in the environment. The large open vertical areas were particularly challenging for my aerial robots. They could easily lose track of their position without enough nearby features to track and it was challenging to find the correct path in and out of such large vertical areas.

How will you be preparing for the SubT Final?

To prepare for the SubT Final the vehicles will be getting a lot smarter. The ground vehicles will be better at navigation and communicating with one another. The aerial vehicles will be better able to handle large vertical areas both from a positioning and a planning point of view. Finally, all of the vehicles will do a better job coordinating what areas have been explored and what areas have good leads for further exploration.

Image: DARPA The final score for the DARPA SubT Cave Circuit virtual competition.

We also had a chance to ask SubT program manager Tim Chung a few questions at yesterday’s post-event press conference, about the course itself and what he thinks teams should have learned from the competition:

IEEE Spectrum: Having looked through some real caves, can you give some examples of some of the most significant differences between this simulation and real caves? And with the enormous variety of caves out there, how generalizable are the solutions that teams came up with?

Tim Chung: Many of the caves that I’ve had to crawl through and gotten bumps and scrapes from had a couple of different features that I’ll highlight. The first is the variations in moisture— a lot of these caves were naturally formed with streams and such, so many of the caves we went to had significant mud, flowing water, and such. And so one of the things we're not capturing in the SubT simulator is explicitly anything that would submerge the robots, or otherwise short any of their systems. So from that perspective, that's one difference that's certainly notable. 

And then the other difference I think is the granularity of the terrain, whether it's rubble, sand, or just raw dirt, friction coefficients are all across the board,  and I think that's one of the things that any terrestrial simulator will both struggle with and potentially benefit from— that is, terramechanics simulation abilities. Given the emphasis on mobility in the SubT simulation, we’re capturing just a sliver of the complexity of terramechanics, but I think that's probably another take away that you'll certainly see—  where there’s that distinction between physical and virtual technologies. 

To answer your second question about generalizability— that’s the multi-million dollar question! It’s definitely at the crux of why we have eight diverse worlds, both in size verticality, dimensions, constraint passageways, etc. But this is eight out of countless variations, and the goal of course is to be able to investigate what those key dependencies are. What I'll say is that the out of the seventy three different virtual cave tiles, which are the building blocks that make up these virtual worlds, quite a number of them were not only inspired by real world caves, but were specifically designed so that we can essentially use these tiles as unit tests going forward. So, if I want to simulate vertical inclines, here are the tiles that are the vertical vertical unit tests for robots, and that’s how we’re trying to to think through how to tease out that generalizability factor. 

What are some observations from this event that you think systems track teams should pay attention to as they prepare for the final event?

One of the key things about the virtual competition is that you submit your software, and that's it. So you have to design everything from state management to failure mode triage, really thinking about what could go wrong and then building out your autonomous capabilities either to react to some of those conditions, or to anticipate them. And to be honest I think that the humans in the loop that we have in the systems competition really are key enablers of their capability, but also could someday (if not already) be a crutch that we might not be able to develop. 

Thinking through some of the failure modes in a fully autonomous software deployed setting are going to be incredibly valuable for the systems competitors, so that for example the human supervisor doesn't have to worry about those failure modes as much, or can respond in a more supervisory way rather than trying to joystick the robot around. I think that's going to be one of the greatest impacts,  thinking through what it means to send these robots off to autonomously get you the information you need and complete the mission

This isn’t to say that the humans aren't going to be useful and continue to play a role of course, but I think this shifting of the role of the human supervisor from being a state manager to being more of a tactical commander will dramatically highlight the impact of the virtual side on the systems side. 

What, if anything, should we take away from one person teams being able to do so consistently well in the virtual circuit? 

It’s a really interesting question. I think part of it has to do with systems integration versus software integration. There's something to be said for the richness of the technologies that can be developed, and how many people it requires to be able to develop some of those technologies. With the systems competitors, having one person try to build, manage, deploy, service, and operate all of those robots is still functionally quite challenging, whereas in the virtual competition, it really is a software deployment more than anything else. And so I think the commonality of single person teams may just be a virtue of the virtual competition not having some of those person-intensive requirements.

In terms of their strong performance, I give credit to all of these really talented folks who are taking upon themselves to jump into the competitor pool and see how well they do, and I think that just goes to show you that whether you're one person or ten people people or a hundred people on a team, a good idea translated and executed well really goes a long way.

Looking ahead, teams have a year to prepare for the final event, which is still scheduled to be held sometime in fall 2021. And even though there was no cave event for systems track teams, the fact that the final event will be a combination of tunnel, urban, and cave circuits means that systems track teams have been figuring out how to get their robots to work in caves anyway, and we’ll be bringing you some of their stories over the next few weeks.

[ DARPA SubT ]

Pages