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.

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!

[ 1X ]

[ Electric Sheep ]

[ 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 ]

[ OmniNxt ]

[ Paper ]

[ 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.”

[ 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.

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.

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.

Enjoy today’s videos!

[ USC ]

[ Naver Labs ]

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

[ Paper ]

[ 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 ]

[ AVFL ]

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

[ Aibo ]

[ Max Planck Institute ]

[ ESA ]

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

[ Paper ] via [ Michigan Robotics ]

Not bad for 2016, right?

[ Namiki Lab ]

[ RILAB ] via [ KIMLAB ]

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

[ UMD ]

When we think about robotic manipulation, the default is usually to think about grippers—about robots using manipulators (like fingers or other end effectors) to interact with objects. For most humans, though, interacting with objects can be a lot more complicated, and we use whatever body parts are convenient to help us deal with objects that are large or heavy or awkward.

This somewhat constrained definition of robotic manipulation isn’t robotics’ fault, really. The word “manipulation” itself comes from the Latin for getting handsy with stuff, so there’s a millennium or two’s-worth of hand-related inertia behind the term. The Los Altos, Calif.-based Toyota Research Institute (TRI) is taking a more expansive view with their new humanoid, Punyo, which uses its soft body to help it manipulate objects that would otherwise be pretty much impossible to manage with grippers alone.

“An anthropomorphic embodiment allows us to explore the complexities of social interactions like physical assistance, non-verbal communication, intent, predictability, and trust, to name just a few.” —Alex Alspach, Toyota Research Institute (TRI)

Punyo started off as just a squishy gripper at TRI, but the idea was always to scale up to a big squishy humanoid, hence this concept art of a squishified T-HR3:

This concept image shows what Toyota’s T-HR3 humanoid might look like when bubble-ized.TRI

“We use the term ‘bubble-ized,’ says Alex Alspach, Tech Lead for Punyo at TRI. Alspach tells us that the concept art above doesn’t necessarily reflect what the Punyo humanoid will eventually look like, but “it gave us some physical constraints and a design language. It also reinforced the idea that we are after general hardware and software solutions that can augment and enable both future and existing robots to take full advantage of their whole bodies for manipulation.”

This version of Punyo isn’t quite at “whole” body manipulation, but it can get a lot done using its arms and chest, which are covered with air bladders that provide both sensing and compliance:

Many of those motions look very human-like, because this is how humans manipulate things. Not to throw too much shade at all those humanoid warehouse robots, but as is pointed out in the video above, using just our hands outstretched in front of us to lift things is not how humans do it, because using other parts of our bodies to provide extra support makes lifting easier. This is not a trivial problem for robots, though, because interactions between point contacts that are rigid (like how most robotics manipulators handle the world) are fairly well understood. Once you throw big squishy surfaces into the mix, along with big squishy objects, it’s just not something that most robots are ready for.

“A soft robot does not interact with the world at a single point.” —Russ Tedrake, TRI

“Current robot manipulation evolved from big, strong industrial robots moving car parts and big tools with their end effectors,” Alspach says. “I think it’s wise to take inspiration from the human form—we are strong enough to perform most everyday tasks with our hands, but when a big, heavy object comes around, we need to get creative with how we wrap our arms around it and position our body to lift it.”

Robots are notorious for lifting big and heavy objects, primarily by manipulating them with robot-y form factors in robot-y ways. So what’s so great about the human form factor, anyway? This question goes way beyond Punyo, of course, but we wanted to get the Punyo team’s take on humanoids, and we tossed a couple more questions at them just for fun.

IEEE Spectrum: So why humanoids?

Alspach: The humanoid robot checks a few important boxes. First of all, the environments we intend to work in were built for humans, so the humanoid form helps a robot make use of the spaces and tools around it. Independently, multiple teams at TRI have converged on bi-manual systems for tasks like grocery shopping and food preparation. A chest between these arms is a simple addition that gives us useful contact surfaces for manipulating big objects, too. Furthermore, our Human-Robot Interaction (HRI) team has done, and continues to do, extensive research with older adults, the people we look forward to helping the most. An anthropomorphic embodiment allows us to explore the complexities of social interactions like physical assistance, non-verbal communication, intent, predictability, and trust, to name just a few.

“We focus not on highly precise tasks but on gross, whole-body manipulation, where robust strategies help stabilize and control objects, and a bit of sloppiness can be an asset.” —Alex Alspach, TRI

Does having a bubble-ized robot make anything more difficult for you?

Russ Tedrake, VP of Robotics Research: If you think of your robot as interacting with the world at a point—the standard view from e.g. impedance control—then putting a soft, passive spring in series between your robot and the world does limit performance. It reduces your control bandwidth. But that view misses the more important point. A soft robot does not interact with the world at a single point. Soft materials fundamentally change the dynamics of contact by deforming around the material—generating patch contacts that allow contact forces and moments not achievable by a rigid interaction.

Alspach: Punyo’s softness is extreme compared to other manipulation platforms that may, say, just have rubber pads on their arms or fingers. This compliance means that when we grab an object, it may not settle exactly where we planned for it to, or, for example, if we bump that object up against the edge of a table, it may move within our grasp. For these reasons, tactile sensing is an important part of our solution as we dig into how to measure and control the state of the objects we manipulate. We focus not on highly precise tasks but on gross, whole-body manipulation, where robust strategies help stabilize and control objects, and a bit of sloppiness can be an asset.

Compliance can be accomplished in different ways, including just in software. What’s the importance of having a robot that’s physically squishy rather than just one that acts squishily?

Andrew Beaulieu, Punyo Tech Lead: We do not believe that passive and active compliance should be considered mutually exclusive, and there are several advantages to having a physically squishy robot, especially when we consider having a robot operate near people and in their spaces. Having a robot that can safely make contact with the world opens up avenues of interaction and exploration. Using compliant materials on the robot also allows it to conform to complicated shapes passively in a way that would otherwise involve more complicated articulated or actuated mechanisms. Conforming to the objects allows us to increase the contact patch with the object and distribute the forces, usually creating a more robust grasp. These compliant surfaces allow us to research planning and control methods that might be less precise, rely less on accurate object localization, or use hardware with less precise control or sensing.

What’s it like to be hugged by Punyo?

Kate Tsui, Punyo HRI Tech Lead: Although Punyo isn’t a social robot, a surprising amount of emotion comes through its hug, and it feels quite comforting. A hug from Punyo feels like a long, sustained, snug squeeze from a close friend you haven’t seen for a long time and don’t want to let go.

A series of concept images shows situations in which whole body manipulation might be useful in the home.TRI

(Interview transcript ends.)

Softness seems like it could be a necessary condition for bipedal humanoids working in close proximity to humans, especially in commercial or home environments where interactions are less structured and predictable. “I think more robots using their whole body to manipulate is coming soon, especially with the recent explosion of humanoids outside of academic labs,” Alspach says. “Capable, general-purpose robotic manipulation is a competitive field, and using the whole body unlocks the ability to efficiently manipulate large, heavy, and unwieldy objects.”

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.

Enjoy today’s videos!

[ Columbia ]

[ Stanford ]

I’m sorry, but the world does not need more drummers.

[ Fourier Intelligence ]

Always good to see NASA’s Valakyrie doing research.

[ NASA ]

[ Paper ]

[ Flyability ]

[ Paper ]

Thanks, Kento!

[ Paper ]

Thanks, Kelly!

[ Agility ]

[ Boston Dynamics ]

[ Paper ]

[ Paper ]

Catch me if you can!

[ CVUT ]

[ CaT ]

[ TED ]

[ JPL ]

[ Penn Engineering ]

At NVIDIA GTC last week, Boston Dynamics CTO Aaron Saunders gave a talk about deploying AI in real world robots—namely, how Spot is leveraging reinforcement learning to get better at locomotion (We spoke with Saunders last year about robots falling over). And Spot has gotten a lot better—a Spot robot takes a tumble on average once every 50 kilometers, even as the Spot fleet collectively walks enough to circle the Earth every three months.

That fleet consists of a lot of commercial deployments, which is impressive for any mobile robot, but part of the reason for that is because the current version of Spot is really not intended for robotics research, even though over 100 universities are home to at least one Spot. Boston Dynamics has not provided developer access to Spot’s joints, meaning that anyone who has wanted to explore quadrupedal mobility has had to find some other platform that’s a bit more open and allows for some experimentation.

Boston Dynamics is now announcing a new variant of Spot that includes a low-level application programming interface (API) that gives joint-level control of the robot. This will give (nearly) full control over how Spot moves its legs, which is a huge opportunity for the robotics community, since we’ll now be able to find out exactly what Spot is capable of. For example, we’ve already heard from a credible source that Spot is capable of running much, much faster than Boston Dynamics has publicly shown, and it’s safe to assume that a speedier Spot is just the start.

An example of a new Spot capability when a custom locomotion controller can be used on the robot.Boston Dynamics

When you buy a Spot robot from Boston Dynamics, it arrives already knowing how to walk. It’s very, very good at walking. Boston Dynamics is so confident in Spot’s walking ability that you’re only allowed high-level control of the robot: You tell it where to go, it decides how to get there. If you want to do robotics research using Spot as a mobility platform, that’s totally fine, but if you want to do research on quadrupedal locomotion, it hasn’t been possible with Spot. But that’s changing.

The Spot RL Researcher Kit is a collaboration between Boston Dynamics, Nvidia, and the AI Institute. It includes a joint-level control API, an Nvidia Jetson AGX Orin payload, and a simulation environment for Spot based on Nvidia Isaac Lab. The kit will be officially released later this year, but Boston Dynamics is starting a slow rollout through an early adopter beta program.

From a certain perspective, Boston Dynamics did this whole thing with Spot backwards by first creating a commercial product and only then making it into a research platform. “At the beginning, we felt like it would be great to include that research capability, but that it wasn’t going to drive the adoption of this technology,” Saunders told us after his GTC session. Instead, Boston Dynamics first focused on getting lots of Spots out into the world in a useful way, and only now, when the company feels like they’ve gotten there, is the time right to unleash a fully-featured research version of Spot. “It was really just getting comfortable with our current product that enabled us to go back and say, ‘how can we now provide people with the kind of access that they’re itching for?’”

Getting to this point has taken a huge amount of work for Boston Dynamics. Predictably, Spot started out as a novelty for most early adopters, becoming a project for different flavors of innovation groups within businesses rather than an industrial asset. “I think there’s been a change there,” Saunders says. “We’re working with operational customers a lot more, and the composure of our sales is shifting away from being dominated by early adopters and we’re starting to see repeat sales and interest in larger fleets of robots.”

Deploying and supporting a large fleet of Spots is one of the things that allowed Boston Dynamics to feel comfortable offering a research version. Researchers are not particularly friendly to their robots, because the goal of research is often to push the envelope of what’s possible. And part of that process includes getting very well acquainted with what turns out to be not possible, resulting in robots that end up on the floor, sometimes in pieces. The research version of Spot will include a mandatory Spot Care Service Plan, which exists to serve commercial customers but will almost certainly provide more value to the research community who want to see what kinds of crazy things they can get Spot to do.

Exactly how crazy those crazy things will be remains to be seen. Boston Dynamics is starting out with a beta program for the research Spots partially because they’re not quite sure yet how many safeguards to put in place within the API. “We need to see where the problems are,” Saunders says. “We still have a little work to do to really hone in how our customers are going to use it.” Deciding how much Spot should be able to put itself at risk in the name of research may be a difficult question to answer, but I’m pretty sure that the beta program participants are going to do their best to find out how much tolerance Boston Dynamics has for Spot shenanigans. I just hope that whatever happens, they share as much video of it as possible.

The Spot Early Adopter Program for the new RL Researcher Kit is open for applications here.

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.

Enjoy today’s videos!

[ NVIDIA ]

[ Boston Dynamics ]

[ 1X ]

What I find interesting about the Unitree H1 doing a standing flip is where it decides to put its legs.

[ Unitree ]

[ Pickle ]

[ ICD-LAB ]

[ Electric Sheep ]

[ DEEP Robotics ]

If you want to collect data for robot grasping, using Stretch and a pair of tongs is about as affordable as it gets.

[ Hello Robot ]

The real reason why Digit’s legs look backwards is so that it doesn’t bang its shins taking GPUs out of the oven.

Meanwhile, some of us can bake our GPUs without even needing an oven.

[ Agility ]

[ LimX ]

And now, this.

[ Suzumori Endo Lab ]

[ Cornell ]

[ DLR ]

[ Clearpath ]

[ KIMLAB ]

[ Paper ] via [ HKUST ]

[ CYBATHLON ]

[ DJI ]

Applying electricity for a few seconds to a soft material, such as a slice of raw tomato or chicken, can strongly bond it to a hard object, such as a graphite slab, without any tape or glue, a new study finds. This unexpected effect is also reversible—switching the direction of the electric current often easily separates the materials, scientists at the University of Maryland say. Potential applications for such “electroadhesion,” which can even work underwater, may include improved biomedical implants and biologically inspired robots.

“It is surprising that this effect was not discovered earlier,” says Srinivasa Raghavan, a professor of chemical and biomolecular engineering at the University of Maryland. “This is a discovery that could have been made pretty much since we’ve had batteries.”

In nature, soft materials such as living tissues are often bonded to hard objects such as bones. Previous research explored chemical ways to accomplish this feat, such as with glues that mimic how mussels stick to rocks and boats. However, these bonds are usually irreversible.

They tried a number of different soft materials, such as tomato, apple, beef, chicken, pork and gelatin...

Previously, Raghavan and his colleagues discovered that electricity could make gels stick to biological tissue, a discovery that might one day lead to gel patches that can help repair wounds. In the new study, instead of bonding two soft materials together, they explored whether electricity could make a soft material stick to a hard object.

The scientists began with a pair of graphite electrodes (consisting of an anode and a cathode) and an acrylamide gel. They applied five volts across the gel for three minutes. Surprisingly, they found the gel strongly bonded onto the graphite anode. Attempts to wrench the gel and electrode apart would typically break the gel, leaving pieces of it on the electrode. The bond could apparently last indefinitely after the voltage was removed, with the researchers keeping samples of gel and electrode stuck together for months.

Howeve, when the researchers switched the polarity of the current, the acrylamide gel detached from the anode. Instead, it adhered onto the other electrode.

Raghavan and his colleagues experimented with this newfound electroadhesion effect a number of different ways. They tried a number of different soft materials, such as tomato, apple, beef, chicken, pork and gelatin, as well as different electrodes, such as copper, lead, tin, nickel, iron, zinc and titanium. They also varied the strength of the voltage and the amount of time it was applied.

The researchers found the amount of salt in the soft material played a strong role in the electroadhesion effect. The salt makes the soft material conductive, and high concentrations of salt could lead gels to adhere to electrodes within seconds.

“It’s surprising how simple this effect is, and how widespread it might be”

The scientists also discovered that metals that are better at giving up their electrons, such as copper, lead and tin, are better at electroadhesion. Conversely, metals that hold onto their electrons strongly, such as nickel, iron, zinc and titanium, fared poorly.

These findings suggest that electroadhesion arises from chemical bonds between the electrode and soft material after they exchange electrons. Depending on the nature of the hard and soft materials, adhesion happened at the anode, cathode, both electrodes, or neither. Boosting the strength of the voltage and the amount of time it was applied typically increased adhesion strength.

“It’s surprising how simple this effect is, and how widespread it might be,” Raghavan says.

Potential applications for electroadhesion may include improving biomedical implants—the ability to bond tissue to steel or titanium could help reinforce implants, the researchers say. Electroadhesion may also help create biologically inspired robots with stiff bone-like skeletons and soft muscle-like elements, they add. They also suggest electroadhesion could lead to new kinds of batteries where soft electrolytes are bonded to hard electrodes, although it’s not clear if such adhesion would make much of a difference to a battery’s performance, Raghavan says.

The researchers also discovered that electroadhesion could occur underwater, which they suggest could open up an even wider range of possible applications for this effect. Typical adhesives do not work underwater, since many cannot spread onto solid surfaces that are submerged in liquids, and even those that can usually only form weak adhesive bonds due to interference from the liquid.

“It’s hard for me to pinpoint one real application for this discovery,” Raghavan says. “It reminds me of the researchers who made the discoveries behind Velcro or Post-it notes—the applications were not obvious to them when the discoveries were made, but the applications did arise over time.”

The scientists detailed their findings online 13 March in the journal ACS Central Science.

Nvidia’s ongoing GTC developer conference in San Jose is, unsurprisingly, almost entirely about AI this year. But in between the AI developments, Nvidia has also made a couple of significant robotics announcements.

First, there’s Project GR00T (with each letter and number pronounced individually so as not to invoke the wrath of Disney), a foundation model for humanoid robots. And secondly, Nvidia has committed to be the founding platinum member of the Open Source Robotics Alliance, a new initiative from the Open Source Robotics Foundation intended to make sure that the Robot Operating System (ROS), a collection of open-source software libraries and tools, has the support that it needs to flourish.

First, let’s talk about GR00T (short for “Generalist Robot 00 Technology”). The way that Nvidia presenters enunciated it letter-by-letter during their talks strongly suggests that in private they just say “Groot.” So the rest of us can also just say “Groot” as far as I’m concerned.

As a “general-purpose foundation model for humanoid robots,” GR00T is intended to provide a starting point for specific humanoid robots to do specific tasks. As you might expect from something being presented for the first time at an Nvidia keynote, it’s awfully vague at the moment, and we’ll have to get into it more later on. Here’s pretty much everything useful that Nvidia has told us so far:

This sounds good, but that “will be” is doing a lot of heavy lifting. Like, there’s a very significant “how” missing here. More specifically, we’ll need a better understanding of what’s underlying this foundation model—is there real robot data under there somewhere, or is it based on a massive amount of simulation? Are the humanoid robotic companies involved contributing data to improve GR00T, or instead training their own models based on it? It’s certainly notable that Nvidia is name-dropping most of the heavy-hitters in commercial humanoids, including 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Figure AI, Fourier Intelligence, Sanctuary AI, Unitree Robotics, and XPENG Robotics. We’ll be able to check in with some of those folks directly this week to hopefully learn more.

On the hardware side, Nvidia is also announcing a new computing platform called Jetson Thor:

Jetson Thor was created as a new computing platform capable of performing complex tasks and interacting safely and naturally with people and machines. It has a modular architecture optimized for performance, power and size. The SoC includes a next-generation GPU based on NVIDIA Blackwell architecture with a transformer engine delivering 800 teraflops of 8-bit floating point AI performance to run multimodal generative AI models like GR00T. With an integrated functional safety processor, a high-performance CPU cluster and 100GB of ethernet bandwidth, it significantly simplifies design and integration efforts.

Speaking of Nvidia’s Blackwell architecture—today the company also unveiled its B200 Blackwell GPU. And to round out the announcements, the chip foundry TSMC and Synopsys, an electronic design automation company, each said they will be moving Nvidia’s inverse lithography tool, cuLitho, into production.

The other big announcement is actually from the Open Source Robotics Foundation, which is launching the Open Source Robotics Alliance (OSRA), a “new initiative to strengthen the governance of our open-source robotics software projects and ensure the health of the Robot Operating System (ROS) Suite community for many years to come.” Nvidia is an inaugural platinum member of the OSRA, but they’re not alone—other platinum members include Intrinsic and Qualcomm. Other significant members include Apex, Clearpath Robotics, Ekumen, eProsima, PickNik, Silicon Valley Robotics, and Zettascale.

“The [Open Source Robotics Foundation] had planned to restructure its operations by broadening community participation and expanding its impact in the larger ROS ecosystem,” explains Vanessa Yamzon Orsi, CEO of the Open Source Robotics Foundation. “The sale of [Open Source Robotics Corporation] was the first step towards that vision, and the launch of the OSRA is the next big step towards that change.”

We had time for a brief Q&A with Orsi to better understand how this will affect the ROS community going forward.

You structured the OSRA to have a mixed membership and meritocratic model like the Linux Foundation—what does that mean, exactly?

Vanessa Yamzon Orsi: We have modeled the OSRA to allow for paths to participation in its activities through both paid memberships (for organizations and their representatives) and the community members who support the projects through their contributions. The mixed model enables participation in the way most appropriate for each organization or individual: contributing funding as a paying member, contributing directly to project development, or both.

What are some benefits for the ROS ecosystem that we can look forward to through OSRA?

Orsi: We expect the OSRA to benefit the OSRF’s projects in three significant ways.

• By providing a stable stream of funding to cover the maintenance and development of the ROS ecosystem.
• By encouraging greater community involvement in development through open processes and open, meritocratic status achievement.
• By bringing greater community involvement in governance and ensuring that all stakeholders have a voice in decision-making.

Why will this be a good thing for ROS users?

About a year ago, Zipline introduced Platform 2, an approach to precision urban drone delivery that combines a large hovering drone with a smaller package-delivery “Droid.” Lowered on a tether from the belly of its parent Zip drone, the Droid contains thrusters and sensors (plus a 2.5- to 3.5-kilogram payload) to reliably navigate itself to a delivery area of just one meter in diameter. The Zip, meanwhile, safely remains hundreds of meters up. After depositing its payload, the Droid rises back up to the drone on its tether, and off they go.

At first glance, the sensor and thruster-packed Droid seems complicated enough to be bordering on impractical, especially when you consider the relative simplicity of other drone delivery solutions, which commonly just drop the package itself on a tether from a hovering drone. I’ve been writing about robots long enough that I’m suspicious of robotic solutions that appear to be overengineered, since that’s always a huge temptation with robotics. Like, is this really the best way of solving a problem, or is it just the coolest way?

We know the folks at Zipline pretty well, though, and they’ve certainly made creative engineering work for them, as we saw when we visited one of their “nests” in rural Rwanda. So as Zipline nears the official launch of Platform 2, we spoke with Zipline cofounder and CTO Keenan Wyrobek, Platform 2 lead Zoltan Laszlo, and industrial designer Gregoire Vandenbussche to understand exactly why they think this is the best way of solving precision urban drone delivery.

First, a quick refresher. Here’s what the delivery sequence with the vertical takeoff and landing (VTOL) Zip and the Droid looks like:

The system has a service radius of about 16 kilometers (10 miles), and it can make deliveries to outdoor spaces of “any meaningful size.” Visual sensors on the Droid find the delivery site and check for obstacles on the way down, while the thrusters compensate for wind and movement of the parent drone. Since the big VTOL Zip remains well out of the way, deliveries are fast, safe, and quiet. But it takes two robots to pull off the delivery rather than just one.

On the other end is the infrastructure required to load and charge these drones. Zipline’s Platform 1 drones require a dedicated base with relatively large launch and recovery systems. With Platform 2, the drone drops the Droid into a large chute attached to the side of a building so that the Droid can be reloaded, after which it pulls the Droid out again and flies off to make the delivery:

“We think it’s the best delivery experience. Not the best drone delivery experience, the best delivery experience,” Zipline’s Wyrobek tells us. That may be true, but the experience also has to be practical and sustainable for Zipline to be successful, so we asked the Zipline team to explain the company’s approach to precision urban delivery.

• Approach to drone delivery
• Concept for Droid design
• Designing for cuteness
• Making pinpoint deliveries

IEEE Spectrum: What problems is Platform 2 solving, and why is it necessary to solve those problems in this specific way?

Keenan Wyrobek: There are literally billions of last-mile deliveries happening every year in [the United States] alone, and our customers have been asking for years for something that can deliver to their homes. With our long-range platform, Platform 1, we can float a package down into your yard on a parachute, but that takes some space. And so one half of the big design challenge was how to get our deliveries precise enough, while the other half was to develop a system that will bolt on to existing facilities, which Platform 1 doesn’t do.

Zoltan Laszlo: Platform 1 can deliver within an area of about two parking spaces. As we started to actually look at the data in urban areas using publicly available lidar surveys, we found that two parking spaces serves a bit more than half the market. We want to be a universal delivery service.

But with a delivery area of 1 meter in diameter, which is what we’re actually hitting in our delivery demonstrations for Platform 2, that gets us into the high 90s for the percentage of people that we can deliver to.

Wyrobek: When we say “urban,” what we’re talking about is three-story sprawl, which is common in many large cities around the world. And we wanted to make sure that our deliveries could be precise enough for places like that.

There are some existing solutions for precision aerial delivery that have been operating at scale with some success, typically by winching packages to the ground from a VTOL drone. Why develop your own technique rather than just going with something that has already been shown to work?

Laszlo: Winching down is the natural extension of being able to hover in place, and when we first started, we were like, “Okay, we’re just going to winch down. This will be great, super easy.”

So we went to our test site in Half Moon Bay [on the Northern California coast] and built a quick prototype of a winch system. But as soon as we lowered a box down on the winch, the wind started blowing it all over the place. And this was from the height of our lift, which is less than 10 meters up. We weren’t even able to stay inside two parking spaces, which told us that something was broken with our approach.

The aircraft can sense the wind, so we thought we’d be able to find the right angle for the delivery and things like that. But the wind where the aircraft is may be different from the wind nearer the ground. We realized that unless we’re delivering to an open field, a package that does not have active wind compensation is going to be very hard to control. We’re targeting high-90th percentile in terms of availability due to weather—even if it’s a pretty blustery day, we still want to be able to deliver.

Wyrobek: This was a wild insight when we really understood that unless it’s a perfect day, using a winch actually takes almost as much space as we use for Platform 1 floating a package down on a parachute.

Engineering test footage of Zipline’s Platform 2 docking system at their test site in Half Moon Bay in California.

How did you arrive at this particular delivery solution for Platform 2?

Laszlo: I don’t remember whose idea it was, but we were playing with a bunch of different options. Putting thrusters on the tether wasn’t even the craziest idea. We had our Platform 1 aircraft, which was reliable, so we started with looking at ways to just make that aircraft deliver more precisely. There was only so much more we could do with passive parachutes, but what does an active, steerable parachute look like? There are remote-controlled paragliding toys out there that we tested, with mixed results—the challenge is to minimize the smarts in your parachute, because there’s a chance you won’t get it back. So then we started some crazy brainstorming about how to reliably retrieve the parachute.

Wyrobek: One idea was that the parachute would come with a self-return envelope that you could stick in the mail. Another idea was that the parachute would be steered by a little drone, and when the package got dropped off, the drone would reel the parachute in and then fly back up into the Zip.

Laszlo: But when we realized that the package has to be able to steer itself, that meant the Zip doesn’t need to be active. The Zip doesn’t need to drive the package, it doesn’t even need to see the package, it just needs to be a point up in the sky that’s holding the package. That let us move from having the Zip 50 feet up, to having it 300 feet up, which is important because it’s a big, heavy drone that we don’t want in our customer’s space. And the final step was adding enough smarts to the thing coming down into your space to figure out where exactly to deliver to, and of course to handle the wind.

Once you knew what you needed to do, how did you get to the actual design of the droid?

Gregoire Vandenbussche: Zipline showed me pretty early on that they were ready to try crazy ideas, and from my experience, that’s extremely rare. When the idea of having this controllable tether with a package attached to it came up, one of my first thoughts was that from a user standpoint, nothing like this exists. And the difficulty of designing something that doesn’t exist is that people will try to identify it according to what they know. So we had to find a way to drive that thinking towards something positive.

Early Droid concept sketches by designer Gregoire Vandenbussche featured legs that would fold up after delivery.Zipline

First we thought about putting words onto it, like “hello” or something, but the reality is that we’re an international company and we need to be able to work everywhere. But there’s one thing that’s common to everyone, and that’s emotions—people are able to recognize certain things as being approachable and adorable, so going in that direction felt like the right thing to do. However, being able to design a robot that gives you that kind of emotion but also flies was quite a challenge. We took inspiration from other things that move in 3D, like sea mammals—things that people will recognize even without thinking about it.

Vandenbussche’s sketches show how the design of the Droid was partially inspired by dolphins.Zipline

Now that you say it, I can definitely see the sea mammal inspiration in the drone.

Vandenbussche: There are two aspects of sea mammals that work really well for our purpose. One of them is simplicity of shape; sea mammals don’t have all that many details. Also, they tend to be optimized for performance. Ultimately, we need that, because we need to be able to fly. And we need to be able to convey to people that the drone is under control. So having something you can tell is moving forward or turning or moving away was very helpful.

Wyrobek: One other insight that we had is that Platform 2 needs to be small to fit into tight delivery spaces, and it needs to feel small when it comes into your personal space, but it also has to be big enough inside to be a useful delivery platform. We tried to leverage the chubby but cute look that baby seals have going on.

The design journey was pretty fun. Gregoire would spend two or three days coming up with a hundred different concept sketches. We’d do a bunch of brainstorming, and then Gregoire would come up with a whole bunch of new directions, and we’d keep exploring. To be clear, no one would describe our functional prototypes from back then as “cute.” But through all this iteration eventually we ended up in an awesome place.

And how do you find that place? When do you know that your robot is just cute enough?

One iteration of the Droid, Vandenbussche determined, looked too technical and intimidating.Zipline

Vandenbussche: It’s finding the balance around what’s realistic and functional. I like to think of industrial design as taking all of the constraints and kind of playing Tetris with them until you get a result that ideally satisfies everybody. I remember at one point looking at where we were, and feeling like we were focusing too much on performance and missing that emotional level. So, we went back a little bit to say, where can we bring this back from looking like a highly technical machine to something that can give you a feeling of approachability?

Laszlo: We spent a fair bit of time on the controls and behaviors of the droid to make sure that it moves in a very approachable and predictable way, so that you know where it’s going ahead of time and it doesn’t behave in unexpected ways. That’s pretty important for how people perceive it.

We did a lot of work on how the droid would descend and approach the delivery site. One concept had the droid start to lower down well before the Zip was hovering directly overhead. We had simulations and renderings, and it looked great. We could do the whole delivery in barely over 20 seconds. But even if the package is far away from you, it still looks scary because [the Zip is] moving faster than you would expect, and you can’t tell exactly where it’s going to deliver. So we deleted all that code, and now it just comes straight down, and people don’t back away from the Droid anymore. They’re just like, “Oh, okay, cool.”

How did you design the thrusters to enable these pinpoint deliveries?

Early tests of the Droid centered around a two-fan version.Zipline

Laszlo: With the thrusters, we knew we wanted to maximize the size of at least one of the fans, because we were almost always going to have to deal with wind. We’re trying to be as quiet as we can, so the key there is to maximize the area of the propeller. Our leading early design was just a box with two fans on it:

Two fans with unobstructed flow meant that it moved great, but the challenge of fitting it inside another aircraft was going to be painful. And it looked big, even though it wasn’t actually that big.

Vandenbussche: It was also pretty intimidating when you had those two fans facing you and the Droid coming toward you.

A single steerable fan [left] that acted like a rudder was simpler in some ways, but as the fan got larger, the gyroscopic effects became hard to manage. Instead of one steerable fan, how about two steerable fans? [right] Omnidirectional motion was possible with this setup, but packaging it inside of a Zip didn’t work.Zipline

Laszlo: We then started looking at configurations with a main fan and a second smaller fan, with the bigger fan at the back pushing forward and the smaller fan at the front providing thrust for turning. The third fan we added relatively late because we didn’t want to add it at all. But we found that [with two fans] the droid would have to spin relatively quickly to align to shifting winds, whereas with a third fan we can just push sideways in the direction that we need.

What kind of intelligence does the Droid have?

The current design of Zipline’s Platform 2 Droid is built around a large thruster in the rear and two smaller thrusters at the front and back.Zipline

Wyrobek: The Droid has its own little autopilot, and there’s a very simple communications system between the two vehicles. You may think that it’s a really complex coordinated control problem, but it’s not: The Zip just kind of hangs out, and the Droid takes care of the delivery. The sensing challenge is for the Droid to find trees and powerlines and things like that, and then find a good delivery site.

Was there ever a point at which you were concerned that the size and weight and complexity would not be worth it?

Wyrobek: Our mindset was to fail fast, to try things and do what we needed to do to convince ourselves that it wasn’t a good path. What’s fun about this kind of iterative process is oftentimes, you try things and you realize that actually, this is better than we thought.

Laszlo: We first thought about the Droid as a little bit of a tax, in that it’s costing us extra weight. But if your main drone can stay high enough up that it avoids trees and buildings, then it can just float around up there. If it gets pushed around by the wind, it doesn’t matter because the Droid can compensate.

Wyrobek: Keeping the Zip at altitude is a big win in many ways. It doesn’t have to spend energy station-keeping, descending, and then ascending again. We just do that with the much smaller Droid, which also makes the hovering phase much shorter. It’s also much more efficient to control the small droid than the large Zip. And having all of the sensors on the Droid very close to the area that you’re delivering to makes that problem easier as well. It may look like a more complex system from the outside, but from the inside, it’s basically making all the hardest problems much easier.

Over the past year, Zipline has set up a bunch of partnerships to make residential deliveries to consumers using Droid starting in 2024, including prescriptions from Cleveland Clinic in Ohio, medical products from WellSpan Health in Pennsylvania, tasty food from Mendocino Farms in California, and a little bit of everything from Walmart starting in Dallas. Zipline’s plan is to kick things off with Platform 2 later this year.

Legendary MIT roboticist Daniela Rus has published a new book called The Heart and the Chip: Our Bright Future with Robots. “There is a robotics revolution underway,” Rus says in the book’s introduction, “one that is already causing massive changes in our society and in our lives.” She’s quite right, of course, and although some of us have been feeling that this is true for decades, it’s arguably more true right now than it ever has been. But robots are difficult and complicated, and the way that their progress is intertwined with the humans that make them and work with them means that these changes won’t come quickly or easily. Rus’ experience gives her a deep and nuanced perspective on robotics’ past and future, and we’re able to share a little bit of that with you here.

Daniela Rus: Should roboticists consider subscribing to their own Hippocratic oath?

The following excerpt is from Chapter 14, entitled “What Could Go Wrong?” Which, let’s be honest, is the right question to ask (and then attempt to conclusively answer) whenever you’re thinking about sending a robot out into the real world.

At several points in this book I’ve mentioned the fictional character Tony Stark, who uses technology to transform himself into the superhero Iron Man. To me this character is a tremendous inspiration, yet I often remind myself that in the story, he begins his career as an MIT-­trained weapons manufacturer and munitions developer. In the 2008 film Iron Man, he changes his ways because he learns that his company’s specialized weapons are being used by terrorists.

Remember, robots are tools. Inherently, they are neither good nor bad; it’s how we choose to use them that matters. In 2022, aerial drones were used as weapons on both sides of devastating wars. Anyone can purchase a drone, but there are regulations for using drones that vary between and within different countries. In the United States, the Federal Aviation Administration requires that all drones be registered, with a few exceptions, including toy models weighing less than 250 grams. The rules also depend on whether the drone is flown for fun or for business. Regardless of regulations, anyone could use a flying robot to inflict harm, just like anyone can swing a hammer to hurt someone instead of driving a nail into a board. Yet drones are also being used to deliver critical medical supplies in hard-­to-­reach areas, track the health of forests, and help scientists like Roger Payne monitor and advocate for at-­risk species. My group collaborated with the modern dance company Pilobolus to stage the first theatrical performance featuring a mix of humans and drones back in 2012, with a robot called Seraph. So, drones can be dancers, too. In Kim Stanley Robinson’s prescient science fiction novel The Ministry for the Future, a swarm of unmanned aerial vehicles is deployed to crash an airliner. I can imagine a flock of these mechanical birds being used in many good ways, too. At the start of its war against Ukraine, Russia limited its citizens’ access to unbiased news and information in hopes of controlling and shaping the narrative around the conflict. The true story of the invasion was stifled, and I wondered whether we could have dispatched a swarm of flying video screens capable of arranging themselves into one giant aerial monitor in the middle of popular city squares across Russia, showing real footage of the war, not merely clips approved by the government. Or, even simpler: swarms of flying digital projectors could have broadcasted the footage on the sides of buildings and walls for all to see. If we had deployed enough, there would have been too many of them to shut down.

There may be variations of Tony Stark passing through my university or the labs of my colleagues around the world, and we need to do whatever we can to ensure these talented young individuals endeavor to have a positive impact on humanity.

The Tony Stark character is shaped by his experiences and steered toward having a positive impact on the world, but we cannot wait for all of our technologists to endure harrowing, life-­changing experiences. Nor can we expect everyone to use these intelligent machines for good once they are developed and moved out into circulation. Yet that doesn’t mean we should stop working on these technologies—­the potential benefits are too great. What we can do is think harder about the consequences and put in place the guardrails to ensure positive benefits. My contemporaries and I can’t necessarily control how these tools are used in the world, but we can do more to influence the people making them.

There may be variations of Tony Stark passing through my university or the labs of my colleagues around the world, and we need to do whatever we can to ensure these talented young individuals endeavor to have a positive impact on humanity. We absolutely must have diversity in our university labs and research centers, but we may be able to do more to shape the young people who study with us. For example, we could require study of the Manhattan Project and the moral and ethical quandaries associated with the phenomenal effort to build and use the atomic bomb. At this point, ethics courses are not a widespread requirement for an advanced degree in robotics or AI, but perhaps they should be. Or why not require graduates to swear to a robotics-­ and AI-­attuned variation on the Hippocratic oath?

The oath comes from an early Greek medical text, which may or may not have been written by the philosopher Hippocrates, and it has evolved over the centuries. Fundamentally, it represents a standard of medical ethics to which doctors are expected to adhere. The most famous of these is the promise to do no harm, or to avoid intentional wrongdoing. I also applaud the oath’s focus on committing to the community of doctors and the necessity of maintaining the sacred bond between teacher and pupils. The more we remain linked as a robotics community, the more we foster and maintain our relationships as our students move out into the world, the more we can do to steer the technology toward a positive future. Today the Hippocratic oath is not a universal requirement for certification as a doctor, and I do not see it functioning that way for roboticists, either. Nor am I the first roboticist or AI leader to suggest this possibility. But we should seriously consider making it standard practice.

In the aftermath of the development of the atomic bomb, when the potential of scientists to do harm was made suddenly and terribly evident, there was some discussion of a Hippocratic oath for scientific researchers. The idea has resurfaced from time to time and rarely gains traction. But science is fundamentally about the pursuit of knowledge; in that sense it is pure. In robotics and AI, we are building things that will have an impact on the world and its people and other forms of life. In this sense, our field is somewhat closer to medicine, as doctors are using their training to directly impact the lives of individuals. Asking technologists to formally recite a version of the Hippocratic oath could be a way to continue nudging our field in the right direction, and perhaps serve as a check on individuals who are later asked to develop robots or AI expressly for nefarious purposes.

Of course, the very idea of what is good or bad, in terms of how a robot is used, depends on where you sit. I am steadfastly opposed to giving armed or weaponized robots autonomy. We cannot and should not trust machine intelligences to make decisions about whether to inflict harm on a person or group of people on their own. Personally, I would prefer that robots never be used to do harm to anyone, but this is now unrealistic. Robots are being used as tools of war, and it is our responsibility to do whatever we can to shape their ethical use. So, I do not separate or divorce myself from reality and operate solely in some utopian universe of happy, helpful robots. In fact, I teach courses on artificial intelligence to national security officials and advise them on the strengths, weaknesses, and capabilities of the technology. I see this as a patriotic duty, and I’m honored to be helping our leaders understand the limitations, strengths, and possibilities of robots and other AI-­enhanced physical systems—­what they can and cannot do, what they should and should not do, and what I believe they must do.

Ultimately, no matter how much we teach and preach about the limitations of technology, the ethics of AI, or the potential dangers of developing such powerful tools, people will make their own choices, whether they are recently graduated students or senior national security leaders. What I hope and teach is that we should choose to do good. Despite the efforts of life extension companies, we all have a limited time on this planet, what the scientist Carl Sagan called our “pale blue dot,” and we should do whatever we can to make the most of that time and have a positive impact on our beautiful environment, and the many people and other species with which we share it. My decades-­long quest to build more intelligent and capable robots has only strengthened my appreciation for—­no, wonder at—­the marvelous creatures that crawl, walk, swim, run, slither, and soar across and around our planet, and the fantastic plants, too. We should not busy ourselves with the work of developing robots that can eliminate these cosmically rare creations. We should focus instead on building technologies to preserve them, and even help them thrive. That applies to all living entities, including the one species that is especially concerned about the rise of intelligent machines.

Excerpted from “The Heart and the Chip: Our Bright Future with Robots”. Copyright 2024 by Daniela Rus, Gregory Mone. Used with permission of the publisher, W.W. Norton & Company. All rights reserved.

Your weekly selection of awesome robot videos

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.

Enjoy today’s videos!

How many quadrupeds can you control with one single locomotion policy? Apparently, the answer is “all of the quadrupeds.”

Look for this at ICRA 2024 in a couple of months!

[ EPFL ]

Thanks, Milad!

Very impressive performance from Figure 01, I think, although as is frequently the case, it’s hard to tell exactly how impressive without more information about exactly what’s going on here.

[ Figure ]

[ Science ] via [ ETHZ RSL ]

Robotic vision can be pretty tricky when you’re cooking, because things can significantly change how they look over time, like with melting butter or an egg being fried. Some new research is tackling this, using a (now ancient?) PR2.

[ JSK Lab ]

Thanks, Kento!

[ IHMC ]

Sustainable fashion enabled by smart textiles shaped by a robot and a heat gun. Multiple styles, multiple sizes, all in one garment!

[ MIT ]

Video of Boston Dynamics’ Stretch from MODEX, with a little sneak peak at the end of what the robot’s next warehouse task might be.

[ Boston Dynamics ]

[ Pickle Robot ]

[ FZI ]

[ MARS Lab ]

When IEEE Spectrum first wrote about Covariant in 2020, it was a new-ish robotics startup looking to apply robotics to warehouse picking at scale through the magic of a single end-to-end neural network. At the time, Covariant was focused on this picking use case, because it represents an application that could provide immediate value—warehouse companies pay Covariant for its robots to pick items in their warehouses. But for Covariant, the exciting part was that picking items in warehouses has, over the last four years, yielded a massive amount of real-world manipulation data—and you can probably guess where this is going.

Today, Covariant is announcing RFM-1, which the company describes as a robotics foundation model that gives robots the “human-like ability to reason.” That’s from the press release, and while I wouldn’t necessarily read too much into “human-like” or “reason,” what Covariant has going on here is pretty cool.

“Foundation model” means that RFM-1 can be trained on more data to do more things—at the moment, it’s all about warehouse manipulation because that’s what it’s been trained on, but its capabilities can be expanded by feeding it more data. “Our existing system is already good enough to do very fast, very variable pick and place,” says Covariant co-founder Pieter Abbeel. “But we’re now taking it quite a bit further. Any task, any embodiment—that’s the long-term vision. Robotics foundation models powering billions of robots across the world.” From the sound of things, Covariant’s business of deploying a large fleet of warehouse automation robots was the fastest way for them to collect the tens of millions of trajectories (how a robot moves during a task) that they needed to train the 8 billion parameter RFM-1 model.

Covariant

“The only way you can do what we’re doing is by having robots deployed in the world collecting a ton of data,” says Abbeel. “Which is what allows us to train a robotics foundation model that’s uniquely capable.”

There have been other attempts at this sort of thing: The RTX project is one recent example. But while RT-X depends on research labs sharing what data they have to create a dataset that’s large enough to be useful, Covariant is doing it alone, thanks to its fleet of warehouse robots. “RT-X is about a million trajectories of data,” Abbeel says, “but we’re able to surpass it because we’re getting a million trajectories every few weeks.”

“By building a valuable picking robot that’s deployed across 15 countries with dozens of customers, we essentially have a data collection machine.” —Pieter Abbeel, Covariant

You can think of the current execution of RFM-1 as a prediction engine for suction-based object manipulation in warehouse environments. The model incorporates still images, video, joint angles, force reading, suction cup strength—everything involved in the kind of robotic manipulation that Covariant does. All of these things are interconnected within RFM-1, which means that you can put any of those things into one end of RFM-1, and out of the other end of the model will come a prediction. That prediction can be in the form of an image, a video, or a series of commands for a robot.

What’s important to understand about all of this is that RFM-1 isn’t restricted to picking only things it’s seen before, or only working on robots it has direct experience with. This is what’s nice about foundation models—they can generalize within the domain of their training data, and it’s how Covariant has been able to scale their business as successfully as they have, by not having to retrain for every new picking robot or every new item. What’s counter-intuitive about these large models is that they’re actually better at dealing with new situations than models that are trained specifically for those situations.

For example, let’s say you want to train a model to drive a car on a highway. The question, Abbeel says, is whether it would be worth your time to train on other kinds of driving anyway. The answer is yes, because highway driving is sometimes not highway driving. There will be accidents or rush hour traffic that will require you to drive differently. If you’ve also trained on driving on city streets, you’re effectively training on highway edge cases, which will come in handy at some point and improve performance overall. With RFM-1, it’s the same idea: Training on lots of different kinds of manipulation—different robots, different objects, and so on—means that any single kind of manipulation will be that much more capable.

In the context of generalization, Covariant talks about RFM-1’s ability to “understand” its environment. This can be a tricky word with AI, but what’s relevant is to ground the meaning of “understand” in what RFM-1 is capable of. For example, you don’t need to understand physics to be able to catch a baseball, you just need to have a lot of experience catching baseballs, and that’s where RFM-1 is at. You could also reason out how to catch a baseball with no experience but an understanding of physics, and RFM-1 is not doing this, which is why I hesitate to use the word “understand” in this context.

But this brings us to another interesting capability of RFM-1: it operates as a very effective, if constrained, simulation tool. As a prediction engine that outputs video, you can ask it to generate what the next couple seconds of an action sequence will look like, and it’ll give you a result that’s both realistic and accurate, being grounded in all of its data. The key here is that RFM-1 can effectively simulate objects that are challenging to simulate traditionally, like floppy things.

Covariant’s Abbeel explains that the “world model” that RFM-1 bases its predictions on is effectively a learned physics engine. “Building physics engines turns out to be a very daunting task to really cover every possible thing that can happen in the world,” Abbeel says. “Once you get complicated scenarios, it becomes very inaccurate, very quickly, because people have to make all kinds of approximations to make the physics engine run on a computer. We’re just doing the large-scale data version of this with a world model, and it’s showing really good results.”

Abbeel gives an example of asking a robot to simulate (or predict) what would happen if a cylinder is placed vertically on a conveyor belt. The prediction accurately shows the cylinder falling over and rolling when the belt starts to move—not because the cylinder is being simulated, but because RFM-1 has seen a lot of things being placed on a lot of conveyor belts.

“Five years from now, it’s not unlikely that what we are building here will be the only type of simulator anyone will ever use.” —Pieter Abbeel, Covariant

This only works if there’s the right kind of data for RFM-1 to train on, so unlike most simulation environments, it can’t currently generalize to completely new objects or situations. But Abbeel believes that with enough data, useful world simulation will be possible. “Five years from now, it’s not unlikely that what we are building here will be the only type of simulator anyone will ever use. It’s a more capable simulator than one built from the ground up with collision checking and finite elements and all that stuff. All those things are so hard to build into your physics engine in any kind of way, not to mention the renderer to make things look like they look in the real world—in some sense, we’re taking a shortcut.”

RFM-1 also incorporates language data to be able to communicate more effectively with humans. Covariant

For Covariant to expand the capabilities of RFM-1 towards that long-term vision of foundation models powering “billions of robots across the world,” the next step is to feed it more data from a wider variety of robots doing a wider variety of tasks. “We’ve built essentially a data ingestion engine,” Abbeel says. “If you’re willing to give us data of a different type, we’ll ingest that too.”

“We have a lot of confidence that this kind of model could power all kinds of robots—maybe with more data for the types of robots and types of situations it could be used in.” —Pieter Abbeel, Covariant

One way or another, that path is going to involve a heck of a lot of data, and it’s going to be data that Covariant is not currently collecting with its own fleet of warehouse manipulation robots. So if you’re, say, a humanoid robotics company, what’s your incentive to share all the data you’ve been collecting with Covariant? “The pitch is that we’ll help them get to the real world,” Covariant co-founder Peter Chen says. “I don’t think there are really that many companies that have AI to make their robots truly autonomous in a production environment. If they want AI that’s robust and powerful and can actually help them enter the real world, we are really their best bet.”

Covariant’s core argument here is that while it’s certainly possible for every robotics company to train up their own models individually, the performance—for anybody trying to do manipulation, at least—would be not nearly as good as using a model that incorporates all of the manipulation data that Covariant already has within RFM-1. “It has always been our long term plan to be a robotics foundation model company,” says Chen. “There was just not sufficient data and compute and algorithms to get to this point—but building a universal AI platform for robots, that’s what Covariant has been about from the very beginning.”

The global ocean is difficult to explore—the common refrain is that we know less about the deep ocean than we do about the surface of the moon. Australian company Advanced Navigation wants to change that with a pint-sized autonomous underwater vehicle (AUV) that it hopes will become the maritime equivalent of a consumer drone. And the new AUV is already getting to work mapping and monitoring Australia’s coral reefs and diving for shipwrecks.

The Sydney-based company has been developing underwater navigation technology for more than a decade. In 2022, Advanced Navigation unveiled its first in-house AUV, called Hydrus. At less than half a meter long, the vehicle is considerably smaller than most alternatives. Even so, it’s fully autonomous and carries a 4k-resolution camera capable of 60 frames per second that can both capture high-definition video and construct detailed 3D photogrammetry models.

Advanced Navigation says Hydrus—with a depth rating of 3,000 meters, a range of 9 kilometers, and a battery that lasts up to three hours—is capable of a wide variety of missions. The company recently sold two units to the Australian Institute of Marine Science (AIMS), the country’s tropical marine science agency, which will use them to survey coral reefs in the North West Shelf region off the country’s west coast. Hydrus has also recently collaborated with the Western Australian Museum to produce a detailed 3D model of a shipwreck off the coast near Perth.

“If people can go and throw one of these off the boat, just like they can throw a drone up in the air, that will obviously benefit the exploration of the sea.” —Ross Anderson, Western Australian Museum

After many years of supplying components to other robotics companies, Peter Baker, subsea product manager at Advanced Navigation, says they company spotted a gap in the market. “We wanted to take the user experience that someone would have with an aerial drone and bring that underwater,” he says. “It’s very expensive to get images and data of the seabed. So by being able to miniaturize this system, and have it drastically simplified from the user’s point of view, it makes data a lot more accessible to people.”

But building a compact and low-cost AUV is not simple. The deep ocean is not a friendly place for electronics, says Baker, due to a combination of high pressure and corrosive seawater. The traditional way of dealing with this is to stick all the critical components in a sealed titanium tube that can maintain ambient pressure and keep moisture out. However, this requires you to add buoyancy to compensate for the extra weight, says Baker, which increases the bulk of the vehicle. That means bigger motors and bigger batteries. “The whole thing spirals up and up until you’ve got something the size of a minibus,” he says.

Advanced Navigation got around the spiral by designing bespoke pressure-tolerant electronics. They built all of their circuit boards from scratch, carefully selecting components that had been tested to destruction in a hydrostatic pressure chamber. These were then encapsulated in a water-proof composite shell, and to further reduce the risk of water ingress the drone operates completely wirelessly. Batteries are recharged using inductive charging and data transfer is either over Wi-Fi when above water or via an optical modem when below the surface.

Hydrus AUVs are charged using induction to keep corrosive seawater from leaking in through charging ports.Advanced Navigation

This has allowed the company to significantly miniaturize the system, says Baker, which has a drastic impact on the overall cost of operations. “You don’t need a crane or a winch or anything like that to recover the vehicle, you can pick it up with a fishing net,” he says. “You can get away with using a much smaller boat, and the rule of thumb in the industry is if you double the size of your boat, you quadruple the cost.”

Just as important, though, is the vehicle’s ease of use. Most underwater robotics systems still operate with a tether, says Baker, but Hydrus carries all the hardware required to support autonomous navigation on board. The company’s “bread and butter” is inertial navigation technology, which uses accelerometers and gyroscopes to track the vehicle from a known starting point. But the drone also features a sonar system that allows it to stay a set distance from the seabed and also judge its speed by measuring the Doppler shift on echoes as they bounce back.

This means that users can simply program in a set of way points on a map, toss the vehicle overboard and leave it to its own devices, says Baker. The Hydrus does have a low-bandwidth acoustic communication channel that allows the operator to send basic commands like “stop” or “come home,” he says, but Hydrus is designed to be a set-and-forget AUV. “That really lowers the thresholds of what a user needs to be able to operate it,” he says. “If you can fly a DJI drone you could fly a Hydrus.”

The company estimates for a typical seabed investigation in water shallow enough for human divers, the Hydrus could be 75 percent cheaper than alternatives. And the savings would go up significantly at greater depths. What’s more, says Baker, the drone’s precise navigation means it can produce much more consistent and repeatable data.

To demonstrate its capabilities, Hydrus’ designers went hunting for shipwrecks in the Rottnest ships graveyard just off the coast near Perth, in Western Australia. The site was a designated spot for scuttling aging ships, says Ross Anderson, curator at Western Australian Museum, but has yet to be fully explored due to the depth of many of the wrecks.

The Advanced Navigation team used the Hydrus to create a detailed 3D model of a sunken “coal hulk”—one of a category of old iron sailing ships that were later converted to floating coal warehouses for steamships. The Western Australian Museum has been unable to identify the vessel so far, but Anderson says these kind of models can be hugely beneficial for carrying out maritime archaeology research, as well as educating people about what’s below the waves.

Advanced Navigation used its new Hydrus drone to create a detailed 3D image of an unidentified “coal hulk” ship in the Rottnest ships graveyard off the western coast of Australia.

Advanced Navigation

Any technology that can simplify the process is greatly welcomed, Anderson adds. “If people can go and throw one of these off the boat, just like they can throw a drone up in the air, that will obviously benefit the exploration of the sea,” he says.

Ease of use was also a big driver behind AIMS’s purchase of two Hydrus drones, says technology development program lead Melanie Olsen, who is also an IEEE senior member. Most of the technology available for marine science is still research-grade and a long way from a polished, professional product.

“When you’re an operational agency like AIMS, you typically don’t have the luxury of spending a lot of time on the back of the boat getting equipment ready,” says Olsen. “You need something that users can turn on and go and it’s just working, as time is of the essence.”

Another benefit of the Hydrus for AIMS is that the drone can operate at greater depths than divers and in conditions that would be dangerous for humans. “Its enabling our researchers to see further down in the water and also operate in more dangerous situations such as at night, or in the presence of threats such as crocodiles or sharks, places where we just wouldn’t be able to collect that data,” says Olsen.

The agency will initially use the drones to survey reefs on Australia’s North West Shelf, including Scott Reef and Ashmore Reef. The goal is to collect regular data data on coral health to monitor the state of the reefs, investigate how they’re being effected by climate change, and hopefully get early warning of emerging problems. But Olsen says they expect that the Hydrus will become standard part of their ocean monitoring toolkit going forward.

This story was updated on 11 March 2024 to correct the year when Advanced Navigation unveiled Hydrus.

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.

Enjoy today’s videos!

[ CMU ]

[ Takahiro Miki ]

Sure, 3.3 meters per second is fast for a humanoid, but I’m more impressed by the spinning around while walking downstairs.

[ Unitree ]

[ SAQIEL ]

Thanks, Temma!

[ Robot Era ]

“Impact-Aware Bimanual Catching of Large-Momentum Objects.” Need I say more?

[ SLMC ]

[ UMass Amherst ]

Thanks, Julia!

The manipulation here is pretty impressive, but it’s hard to know how impressive without also knowing how much the video was sped up.

[ Somatic ]

[ DJI ]

[ Extend Robotics ]

[ Cybathlon ]

It’s been a long road to electrically powered robots.

[ ABB ]

[ CRASAR ]

It probably will not surprise you that there are a lot of robots involved in building Rivian trucks and vans.

[ Kawasaki Robotics ]

[ DARPA ]

You’ve seen this before: a truck unloading robot that’s made up of a mobile base with an arm on it that drives up into the back of a trailer and then uses suction to grab stacked boxes and put them onto a conveyor belt. We’ve written about a couple of the companies doing this, and there are even more out there. It’s easy to understand why—trailer unloading involves a fairly structured and controlled environment with a very repetitive task, it’s a hard job that sucks for humans, and there’s an enormous amount of demand.

While it’s likely true that there’s enough room for a whole bunch of different robotics companies in the trailer unloading space, a given customer is probably only going to pick one, and they’re going to pick the one that offers the right combination of safety, capability, and cost. Anyware Robotics thinks they have that mix, aided by a box handling solution that is both very clever and so obvious that I’m wondering why I didn’t think of it myself.

The overall design of Pixmo itself is fairly standard as far as trailer unloading robots go, but some of the details are interesting. We’re told that Pixmo is the only trailer unloading system that integrates a heavy-payload collaborative arm, actually a fairly new commercial arm from Fanuc. This means that Anyware Robotics doesn’t have to faff about with their own hardware, and also that their robot is arguably safer, being ISO certified safe to work directly with people. The base is custom, but Anyware is contracting it out to a big robotics OEM.

“We’ve put a lot of effort into making sure that most of the components of our robot are off-the-shelf,” co-founder and CEO Thomas Tang tells us. “There are already so many mature and cost-efficient suppliers that we want to offload the supply chain, the certification, the reliability testing onto someone else’s shoulders.” And while there are a selection of automated mobile robots (AMRs) out there that seem like they could get the job done, the problem is that they’re all designed for flat surfaces, and getting into and out of the back of the trailer often involves a short, steep ramp, hence the need for their own design. Even with the custom base, Tang says that Pixmo is very cost efficient, and the company predicts that it will be approximately one third the cost of other solutions with a payback of about 24 months.

But here’s the really clever bit:

Anyware Robotics Pixmo Trailer Unloading

That conveyor system in front of the boxes is an add-on that’s used in support of Pixmo. There are two benefits here: first, having the conveyor add-on aligned with the base of a box minimizes the amount of lifting that Pixmo has to do. This allows Pixmo to handle boxes of up to 65 lbs with a lift-and-slide technique, putting it at the top end of trailer unloading robot payload. And the second benefit is that the add-on system decreases the distance that Pixmo has to move the box to just about as small as it can possibly be, eliminating the need for the arm to rotate around to place a box on a conveyor next to or behind itself. Lowering this cycle time means that Pixmo can achieve a throughput of up to 1,000 boxes per hour—about one box every four seconds, which the Internet suggests is quite fast, even for a professional human. Anyware Robotics is introducing this add-on system at MODEX next week, and they have a patent pending on the idea.

This seems like such a simple, useful idea that I asked Tang why they were the first ones to come up with it. “In robotics startups, there tends to be a legacy mindset issue,” Tang told me. “When people have been working on robot arms for so many years, we just think about how to use robot arms to solve everything. That’s maybe that’s the reason why other companies didn’t come up with this solution.” Tang says that Anyware started with much more complicated add-on designs before finding this solution. “Usually it’s the most simple solution that has the most trial and error behind it.”

Anyware Robotics is focused on trailer unloading for now, but Pixmo could easily be adapted for palletizing and depalletizing or somewhat less easily for other warehouse tasks like order picking or machine tending. But why stop there? A mobile manipulator can (theoretically) do it all (almost), and that’s exactly what Tang wants:

At just over one year old, Anyware has already managed to complete a pilot program (and convert it to a purchase order). They’re currently in the middle of several other pilot programs with leading third-party logistics providers, and they expect to spend the next several months focusing on productization with the goal of releasing the first commercial version of Pixmo by July of this year.

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.

Enjoy today’s videos!

Figure has raised a US $675 million Series B, valuing the company at $2.6 billion.

[ Figure ]

Meanwhile, here’s how things are going at Agility Robotics, whose last raise was a $150 million Series B in April of 2022.

[ Agility Robotics ]

Also meanwhile, here’s how things are going at Sanctuary AI, whose last raise was a $58.5 million Series A in March of 2022.

[ Sanctuary AI ]

[ UBTECH ]

Henry Evans keeps working hard to make robots better, this time with the assistance of researchers from Carnegie Mellon University.

[ HAT2 ] via [ CMU ]

Watch this video for the three glorious seconds at the end.

[ Tech United ]

Oh, it’s HOOSKvarna, not HUSKvarna.

[ Husqvarna ] via [ Engadget ]

[ Engineered Arts ]

[ NYU ]

[ Boston Dynamics ]

[ Freeform Robotics ]

[ DLR ]

[ CYBATHLON ]

[ WeRobotics ]

In case you’d forgotten, Amazon has a lot of robots.

[ Amazon Robotics ]

[ ABB ]

[ Ingenuity Labs ]

Today, Figure is announcing an astonishing US $675 million Series B raise, which values the company at an even more astonishing $2.6 billion. Figure is one of the companies working towards a multi or general purpose (depending on who you ask) bipedal or humanoid (depending on who you ask) robot. The astonishing thing about this valuation is that Figure’s robot is still very much in the development phase—although they’re making rapid progress, which they demonstrate in a new video posted this week.

This round of funding comes from Microsoft, OpenAI Startup Fund, Nvidia, Jeff Bezos (through Bezos Expeditions), Parkway Venture Capital, Intel Capital, Align Ventures, and ARK Invest. Figure says that they’re going to use this new capital “for scaling up AI training, robot manufacturing, expanding engineering headcount, and advancing commercial deployment efforts.” In addition, Figure and OpenAI will be collaborating on the development of “next generation AI models for humanoid robots” which will “help accelerate Figure’s commercial timeline by enhancing the capabilities of humanoid robots to process and reason from language.”

As far as that commercial timeline goes, here’s the most recent update:

Figure

And to understand everything that’s going on here, we sent a whole bunch of questions to Jenna Reher, Senior Robotics/AI Engineer at Figure.

What does “fully autonomous” mean, exactly?

Jenna Reher: In this case, we simply put the robot on the ground and hit go on the task with no other user input. What you see is using a learned vision model for bin detection that allows us to localize the robot relative to the target bin and get the bin pose. The robot can then navigate itself to within reach of the bin, determine grasp points based on the bin pose, and detect grasp success through the measured forces on the hands. Once the robot turns and sees the conveyor the rest of the task rolls out in a similar manner. By doing things in this way we can move the bins and conveyor around in the test space or start the robot from a different position and still complete the task successfully.

How many takes did it take to get this take?

Reher: We’ve been running this use case consistently for some time now as part of our work in the lab, so we didn’t really have to change much for the filming here. We did two or three practice runs in the morning and then three filming takes. All of the takes were successful, so the extras were to make sure we got the cleanest one to show.

What’s back in the Advanced Actuator Lab?

Reher: We have an awesome team of folks working on some exciting custom actuator designs for our future robots, as well as supporting and characterizing the actuators that went into our current robots.

That’s a very specific number for “speed vs human.” Which human did you measure the robot’s speed against?

Reher: We timed Brett [Adcock, founder of Figure] and a few poor engineers doing the task and took the average to get a rough baseline. If you are observant, that seemingly over-specific number is just saying we’re at 1/6 human speed. The main point that we’re trying to make here is that we are aware we are currently below human speed, and it’s an important metric to track as we improve.

What’s the tether for?

Reher: For this task we currently process the camera data off-robot while all of the behavior planning and control happens onboard in the computer that’s in the torso. Our robots should be fully tetherless in the near future as we finish packaging all of that onboard. We’ve been developing behaviors quickly in the lab here at Figure in parallel to all of the other systems engineering and integration efforts happening, so hopefully folks notice all of these subtle parallel threads converging as we try to release regular updates.

How the heck do you keep your robotics lab so clean?

Reher: Everything we’ve filmed so far is in our large robot test lab, so it’s a lot easier to keep the area clean when people’s desks aren’t intruding in the space. Definitely no guarantees on that level of cleanliness if the camera were pointed in the other direction!

Is the robot in the background doing okay?

Reher: Yes! The other robot was patiently standing there in the background, waiting for the filming to finish up so that our manipulation team could get back to training it to do more manipulation tasks. We hope we can share some more developments with that robot as the main star in the near future.

What would happen if I put a single bowling ball into that tote?

Reher: A bowling ball is particularly menacing to this task primarily due to the moving mass, in addition to the impact if you are throwing it in. The robot would in all likelihood end up dropping the tote, stay standing, and abort the task. With what you see here, we assume that the mass of the tote is known a-priori so that our whole body controller can compensate for the external forces while tracking the manipulation task. Reacting to and estimating larger unknown disturbances such as this is a challenging problem, but we’re definitely working on it.

Tell me more about that very zen arm and hand pose that the robot adopts after putting the tote on the conveyor.

Reher: It does look kind of zen! If you re-watch our coffee video you’ll notice the same pose after the robot gets things brewing. This is a reset pose that our controller will go into between manipulation tasks while the robot is awaiting commands to execute either an engineered behavior or a learned policy.

Are the fingers less fragile than they look?

Reher: They are more robust than they look, but not impervious to damage by any means. The design is pretty modular which is great, meaning that if we damage one or two fingers there is a small number of parts to swap to get everything back up and running. The current fingers won’t necessarily survive a direct impact from a bad fall, but can pick up totes and do manipulation tasks all day without issues.

Is the Figure logo footsteps?