Tag Archives: humans

#437630 How Toyota Research Envisions the Future ...

Posted on November 22, 2020 by Android

Yesterday, the Toyota Research Institute (TRI) showed off some of the projects that it’s been working on recently, including a ceiling-mounted robot that could one day help us with household chores. That system is just one example of how TRI envisions the future of robotics and artificial intelligence. As TRI CEO Gill Pratt told us, the company is focusing on robotics and AI technology for “amplifying, rather than replacing, human beings.” In other words, Toyota wants to develop robots not for convenience or to do our jobs for us, but rather to allow people to continue to live and work independently even as we age.

To better understand Toyota’s vision of robotics 15 to 20 years from now, it’s worth watching the 20-minute video below, which depicts various scenarios “where the application of robotic capabilities is enabling members of an aging society to live full and independent lives in spite of the challenges that getting older brings.” It’s a long video, but it helps explains TRI’s perspective on how robots will collaborate with humans in our daily lives over the next couple of decades.

Those are some interesting conceptual telepresence-controlled bipeds they’ve got running around in that video, right?

For more details, we sent TRI some questions on how it plans to go from concepts like the ones shown in the video to real products that can be deployed in human environments. Below are answers from TRI CEO Gill Pratt, who is also chief scientist for Toyota Motor Corp.; Steffi Paepcke, senior UX designer at TRI; and Max Bajracharya, VP of robotics at TRI.

IEEE Spectrum: TRI seems to have a more explicit focus on eventual commercialization than most of the robotics research that we cover. At what point TRI starts to think about things like reliability and cost?

Photo: TRI

Toyota is exploring robots capable of manipulating dishes in a sink and a dishwasher, performing experiments and simulations to make sure that the robots can handle a wide range of conditions.

Gill Pratt: It’s a really interesting question, because the normal way to think about this would be to say, well, both reliability and cost are product development tasks. But actually, we need to think about it at the earliest possible stage with research as well. The hardware that we use in the laboratory for doing experiments, we don’t worry about cost there, or not nearly as much as you’d worry about for a product. However, in terms of what research we do, we very much have to think about, is it possible (if the research is successful) for it to end up in a product that has a reasonable cost. Because if a customer can’t afford what we come up with, maybe it has some academic value but it’s not actually going to make a difference in their quality of life in the real world. So we think about cost very much from the beginning.

The same is true with reliability. Right now, we’re working very hard to make our control techniques robust to wide variations in the environment. For instance, in work that Russ Tedrake is doing with manipulating dishes in a sink and a dishwasher, both in physical testing and in simulation, we’re doing thousands and now millions of different experiments to make sure that we can handle the edge cases and it works over a very wide range of conditions.

A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time. Some researchers have been very good about showing the blooper reel too, to show that some of the time, robots don’t work.

“A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time.”
—Gill Pratt, TRI

In the spirit of sharing things that didn’t work, can you tell us a bit about some of the robots that TRI has had under development that didn’t make it into the demo yesterday because they were abandoned along the way?

Steffi Paepcke: We’re really looking at how we can connect people; it can be hard to stay in touch and see our loved ones as much as we would like to. There have been a few prototypes that we’ve worked on that had to be put on the shelf, at least for the time being. We were exploring how to use light so that people could be ambiently aware of one another across distances. I was very excited about that—the internal name was “glowing orb.” For a variety of reasons, it didn’t work out, but it was really fascinating to investigate different modalities for keeping in touch.

Another prototype we worked on—we found through our research that grocery shopping is obviously an important part of life, and for a lot of older adults, it’s not necessarily the right answer to always have groceries delivered. Getting up and getting out of the house keeps you physically active, and a lot of people prefer to continue doing it themselves. But it can be challenging, especially if you’re purchasing heavy items that you need to transport. We had a prototype that assisted with grocery shopping, but when we pivoted our focus to Japan, we found that the inside of a Japanese home really needs to stay inside, and the outside needs to stay outside, so a robot that traverses both domains is probably not the right fit for a Japanese audience, and those were some really valuable lessons for us.

Photo: TRI

Toyota recently demonstrated a gantry robot that would hang from the ceiling to perform tasks like wiping surfaces and clearing clutter.

I love that TRI is exploring things like the gantry robot both in terms of near-term research and as part of its long-term vision, but is a robot like this actually worth pursuing? Or more generally, what’s the right way to compromise between making an environment robot friendly, and asking humans to make changes to their homes?

Max Bajracharya: We think a lot about the problems that we’re trying to address in a holistic way. We don’t want to just give people a robot, and assume that they’re not going to change anything about their lifestyle. We have a lot of evidence from people who use automated vacuum cleaners that people will adapt to the tools you give them, and they’ll change their lifestyle. So we want to think about what is that trade between changing the environment, and giving people robotic assistance and tools.

We certainly think that there are ways to make the gantry system plausible. The one you saw today is obviously a prototype and does require significant infrastructure. If we’re going to retrofit a home, that isn’t going to be the way to do it. But we still feel like we’re very much in the prototype phase, where we’re trying to understand whether this is worth it to be able to bypass navigation challenges, and coming up with the pros and cons of the gantry system. We’re evaluating whether we think this is the right approach to solving the problem.

To what extent do you think humans should be either directly or indirectly in the loop with home and service robots?

Bajracharya: Our goal is to amplify people, so achieving this is going to require robots to be in a loop with people in some form. One thing we have learned is that using people in a slow loop with robots, such as teaching them or helping them when they make mistakes, gives a robot an important advantage over one that has to do everything perfectly 100 percent of the time. In unstructured human environments, robots are going to encounter corner cases, and are going to need to learn to adapt. People will likely play an important role in helping the robots learn. Continue reading →

Posted in Human Robots

#437624 AI-Powered Drone Learns Extreme ...

Posted on November 22, 2020 by Android

Quadrotors are among the most agile and dynamic machines ever created. In the hands of a skilled human pilot, they can do some astonishing series of maneuvers. And while autonomous flying robots have been getting better at flying dynamically in real-world environments, they still haven’t demonstrated the same level of agility of manually piloted ones.

Now researchers from the Robotics and Perception Group at the University of Zurich and ETH Zurich, in collaboration with Intel, have developed a neural network training method that “enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.” Extreme.

There are two notable things here: First, the quadrotor can do these extreme acrobatics outdoors without any kind of external camera or motion-tracking system to help it out (all sensing and computing is onboard). Second, all of the AI training is done in simulation, without the need for an additional simulation-to-real-world (what researchers call “sim-to-real”) transfer step. Usually, a sim-to-real transfer step means putting your quadrotor into one of those aforementioned external tracking systems, so that it doesn’t completely bork itself while trying to reconcile the differences between the simulated world and the real world, where, as the researchers wrote in a paper describing their system, “even tiny mistakes can result in catastrophic outcomes.”

To enable “zero-shot” sim-to-real transfer, the neural net training in simulation uses an expert controller that knows exactly what’s going on to teach a “student controller” that has much less perfect knowledge. That is, the simulated sensory input that the student ends up using as it learns to follow the expert has been abstracted to present the kind of imperfect, imprecise data it’s going to encounter in the real world. This can involve things like abstracting away the image part of the simulation until you’d have no way of telling the difference between abstracted simulation and abstracted reality, which is what allows the system to make that sim-to-real leap.

The simulation environment that the researchers used was Gazebo, slightly modified to better simulate quadrotor physics. Meanwhile, over in reality, a custom 1.5-kilogram quadrotor with a 4:1 thrust to weight ratio performed the physical experiments, using only a Nvidia Jetson TX2 computing board and an Intel RealSense T265, a dual fisheye camera module optimized for V-SLAM. To challenge the learning system, it was trained to perform three acrobatic maneuvers plus a combo of all of them:

Image: University of Zurich/ETH Zurich/Intel

Reference trajectories for acrobatic maneuvers. Top row, from left: Power Loop, Barrel Roll, and Matty Flip. Bottom row: Combo.

All of these maneuvers require high accelerations of up to 3 g’s and careful control, and the Matty Flip is particularly challenging, at least for humans, because the whole thing is done while the drone is flying backwards. Still, after just a few hours of training in simulation, the drone was totally real-world competent at these tricks, and could even extrapolate a little bit to perform maneuvers that it was not explicitly trained on, like doing multiple loops in a row. Where humans still have the advantage over drones is (as you might expect since we’re talking about robots) is quickly reacting to novel or unexpected situations. And when you’re doing this sort of thing outdoors, novel and unexpected situations are everywhere, from a gust of wind to a jealous bird.

For more details, we spoke with Antonio Loquercio from the University of Zurich’s Robotics and Perception Group.

IEEE Spectrum: Can you explain how the abstraction layer interfaces with the simulated sensors to enable effective sim-to-real transfer?

Antonio Loquercio: The abstraction layer applies a specific function to the raw sensor information. Exactly the same function is applied to the real and simulated sensors. The result of the function, which is “abstracted sensor measurements,” makes simulated and real observation of the same scene similar. For example, suppose we have a sequence of simulated and real images. We can very easily tell apart the real from the simulated ones given the difference in rendering. But if we apply the abstraction function of “feature tracks,” which are point correspondences in time, it becomes very difficult to tell which are the simulated and real feature tracks, since point correspondences are independent of the rendering. This applies for humans as well as for neural networks: Training policies on raw images gives low sim-to-real transfer (since images are too different between domains), while training on the abstracted images has high transfer abilities.

How useful is visual input from a camera like the Intel RealSense T265 for state estimation during such aggressive maneuvers? Would using an event camera substantially improve state estimation?

Our end-to-end controller does not require a state estimation module. It shares however some components with traditional state estimation pipelines, specifically the feature extractor and the inertial measurement unit (IMU) pre-processing and integration function. The input of the neural networks are feature tracks and integrated IMU measurements. When looking at images with low features (for example when the camera points to the sky), the neural net will mainly rely on IMU. When more features are available, the network uses to correct the accumulated drift from IMU. Overall, we noticed that for very short maneuvers IMU measurements were sufficient for the task. However, for longer ones, visual information was necessary to successfully address the IMU drift and complete the maneuver. Indeed, visual information reduces the odds of a crash by up to 30 percent in the longest maneuvers. We definitely think that event camera can improve even more the current approach since they could provide valuable visual information during high speed.

“The Matty Flip is probably one of the maneuvers that our approach can do very well … It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.”
—Antonio Loquercio, University of Zurich

You describe being able to train on “maneuvers that stretch the abilities of even expert human pilots.” What are some examples of acrobatics that your drones might be able to do that most human pilots would not be capable of?

The Matty Flip is probably one of the maneuvers that our approach can do very well, but human pilots find very challenging. It basically entails doing a high speed power loop by always looking backward. It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.

What are the limits to the performance of this system?

At the moment the main limitation is the maneuver duration. We never trained a controller that could perform maneuvers longer than 20 seconds. In the future, we plan to address this limitation and train general controllers which can fly in that agile way for significantly longer with relatively small drift. In this way, we could start being competitive against human pilots in drone racing competitions.

Can you talk about how the techniques developed here could be applied beyond drone acrobatics?

The current approach allows us to do acrobatics and agile flight in free space. We are now working to perform agile flight in cluttered environments, which requires a higher degree of understanding of the surrounding with respect to this project. Drone acrobatics is of course only an example application. We selected it because it makes a stress test of the controller performance. However, several other applications which require fast and agile flight can benefit from our approach. Examples are delivery (we want our Amazon packets always faster, don’t we?), search and rescue, or inspection. Going faster allows us to cover more space in less time, saving battery costs. Indeed, agile flight has very similar battery consumption of slow hovering for an autonomous drone.

“Deep Drone Acrobatics,” by Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza from the Robotics and Perception Group at the University of Zurich and ETH Zurich, and Intel’s Intelligent Systems Lab, was presented at RSS 2020. Continue reading →

Posted in Human Robots

#437608 Video Friday: Agility Robotics Raises ...

Posted on November 21, 2020 by Android

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

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

Digit is now in full commercial production and we’re excited to announce a $20M funding rounding round co-led by DCVC and Playground Global!

Digits for everyone!

[ Agility Robotics ]

A flexible rover that has both ability to travel long distances and rappel down hard-to-reach areas of scientific interest has undergone a field test in the Mojave Desert in California to showcase its versatility. Composed of two Axel robots, DuAxel is designed to explore crater walls, pits, scarps, vents and other extreme terrain on the moon, Mars and beyond.

This technology demonstration developed at NASA’s Jet Propulsion Laboratory in Southern California showcases the robot’s ability to split in two and send one of its halves — a two-wheeled Axle robot — over an otherwise inaccessible slope, using a tether as support and to supply power.

The rappelling Axel can then autonomously seek out areas to study, safely overcome slopes and rocky obstacles, and then return to dock with its other half before driving to another destination. Although the rover doesn’t yet have a mission, key technologies are being developed that might, one day, help us explore the rocky planets and moons throughout the solar system.

[ JPL ]

A rectangular robot as tiny as a few human hairs can travel throughout a colon by doing back flips, Purdue University engineers have demonstrated in live animal models. Why the back flips? Because the goal is to use these robots to transport drugs in humans, whose colons and other organs have rough terrain. Side flips work, too. Why a back-flipping robot to transport drugs? Getting a drug directly to its target site could remove side effects, such as hair loss or stomach bleeding, that the drug may otherwise cause by interacting with other organs along the way.

[ Purdue ]

This video shows the latest results in the whole-body locomotion control of the humanoid robot iCub achieved by the Dynamic Interaction Control line at IIT-Istituto Italiano di Tecnologia in Genova (Italy). In particular, the iCub now keeps the balance while walking and receiving pushes from an external user. The implemented control algorithms also ensure the robot to remain compliant during locomotion and human-robot interaction, a fundamental property to lower the possibility to harm humans that share the robot surrounding environment.

This is super impressive, considering that iCub was only able to crawl and was still tethered not too long ago. Also, it seems to be blinking properly now, so it doesn’t look like it’s always sleepy.

[ IIT ]

This video shows a set of new tests we performed on Bolt. We conducted tests on 5 different scenarios, 1) walking forward/backward 2) uneven surface 3) soft surface 4) push recovery 5) slippage recovery. Thanks to our feedback control based on Model Predictive Control, the robot can perform walking in the presence of all these uncertainties. We will open-source all the codes in a near future.

[ ODRI ]

The title of this video is “Can you throw your robot into a lake?” The title of this video should be, “Can you throw your robot into a lake and drive it out again?”

[ Norlab ]

AeroVironment Successfully Completes Sunglider Solar HAPS Stratospheric Test Flight, Surpassing 60,000 Feet Altitude and Demonstrating Broadband Mobile Connectivity.

[ AeroVironment ]

We present CoVR, a novel robotic interface providing strong kinesthetic feedback (100 N) in a room-scale VR arena. It consists of a physical column mounted on a 2D Cartesian ceiling robot (XY displacements) with the capacity of (1) resisting to body-scaled users actions such as pushing or leaning; (2) acting on the users by pulling or transporting them as well as (3) carrying multiple potentially heavy objects (up to 80kg) that users can freely manipulate or make interact with each other.

[ DeepAI ]

In a new video, personnel from Swiss energy supply company Kraftwerke Oberhasli AG (KWO) explain how they were able to keep employees out of harm’s way by using Flyability’s Elios 2 to collect visual data while building a new dam.

[ Flyability ]

Enjoy our Ascento robot fail compilation! With every failure we experience, we learn more and we can improve our robot for its next iteration, which will come soon… Stay tuned for more!

FYI posting a robot fails video will pretty much guarantee you a spot in Video Friday!

[ Ascento ]

Humans are remarkably good at using chopsticks. The Guinness World Record witnessed a person using chopsticks to pick up 65 M&Ms in just a minute. We aim to collect demonstrations from humans and to teach robot to use chopsticks.

[ UW Personal Robotics Lab ]

A surprising amount of personality from these Yaskawa assembly robots.

[ Yaskawa ]

This paper presents the system design, modeling, and control of the Aerial Robotic Chain Manipulator. This new robot design offers the potential to exert strong forces and moments to the environment, carry and lift significant payloads, and simultaneously navigate through narrow corridors. The presented experimental studies include a valve rotation task, a pick-and-release task, and the verification of load oscillation suppression to demonstrate the stability and performance of the system.

[ ARL ]

Whether animals or plants, whether in the water, on land or in the air, nature provides the model for many technical innovations and inventions. This is summed up in the term bionics, which is a combination of the words ‘biology‘ and ‘electronics’. At Festo, learning from nature has a long history, as our Bionic Learning Network is based on using nature as the source for future technologies like robots, assistance systems or drive solutions.

[ Festo ]

Dogs! Selfies! Thousands of LEGO bricks! This video has it all.

[ LEGO ]

An IROS workshop talk on “Cassie and Mini Cheetah Autonomy” by Maani Ghaffari and Jessy Grizzle from the University of Michigan.

[ Michigan Robotics ]

David Schaefer’s Cozmo robots are back with this mind-blowing dance-off!

What you just saw represents hundreds of hours of work, David tells us: “I wrote over 10,000 lines of code to create the dance performance as I had to translate the beats per minute of the song into motor rotations in order to get the right precision needed to make the moves look sharp. The most challenging move was the SpongeBob SquareDance as any misstep would send the Cozmos crashing into each other. LOL! Fortunately for me, Cozmo robots are pretty resilient.”

[ Life with Cozmo ]

Thanks David!

This week’s GRASP on Robotics seminar is by Sangbae Kim from MIT, on “Robots with Physical Intelligence.”

While industrial robots are effective in repetitive, precise kinematic tasks in factories, the design and control of these robots are not suited for physically interactive performance that humans do easily. These tasks require ‘physical intelligence’ through complex dynamic interactions with environments whereas conventional robots are designed primarily for position control. In order to develop a robot with ‘physical intelligence’, we first need a new type of machines that allow dynamic interactions. This talk will discuss how the new design paradigm allows dynamic interactive tasks. As an embodiment of such a robot design paradigm, the latest version of the MIT Cheetah robots and force-feedback teleoperation arms will be presented.

[ GRASP ]

This week’s CMU Ri Seminar is by Kevin Lynch from Northwestern, on “Robotics and Biosystems.”

Research at the Center for Robotics and Biosystems at Northwestern University encompasses bio-inspiration, neuromechanics, human-machine systems, and swarm robotics, among other topics. In this talk I will give an overview of some of our recent work on in-hand manipulation, robot locomotion on yielding ground, and human-robot systems.

[ CMU RI ] Continue reading →

Posted in Human Robots

#437598 Video Friday: Sarcos Is Developing a New ...

Posted on November 20, 2020 by Android

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

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.

NASA’s Origins, Spectral Interpretation, Resource Identification, Security, Regolith Explorer (OSIRIS-REx) spacecraft unfurled its robotic arm Oct. 20, 2020, and in a first for the agency, briefly touched an asteroid to collect dust and pebbles from the surface for delivery to Earth in 2023.

[ NASA ]

New from David Zarrouk’s lab at BGU is AmphiSTAR, which Zarrouk describes as “a kind of a ground-water drone inspired by the cockroaches (sprawling) and by the Basilisk lizard (running over water). The robot hovers due to the collision of its propellers with the water (hydrodynamics not aerodynamics). The robot can crawl and swim at high and low speeds and smoothly transition between the two. It can reach 3.5 m/s on ground and 1.5m/s in water.”

AmphiSTAR will be presented at IROS, starting next week!

[ BGU ]

This is unfortunately not a great video of a video that was taken at a SoftBank Hawks baseball game in Japan last week, but it’s showing an Atlas robot doing an honestly kind of impressive dance routine to support the team.

ロボット応援団に人型ロボット『ATLAS』がアメリカからリモートで緊急参戦！！！
ホークスビジョンの映像をお楽しみ下さい♪#sbhawks #Pepper #spot pic.twitter.com/6aTYn8GGli
— 福岡ソフトバンクホークス（公式） (@HAWKS_official)
October 16, 2020

Editor’s Note: The tweet embed above is not working for some reason—see the video here.

[ SoftBank Hawks ]

Thanks Thomas!

Sarcos is working on a new robot, which looks to be the torso of their powered exoskeleton with the human relocated somewhere else.

[ Sarcos ]

The biggest holiday of the year, International Sloth Day, was on Tuesday! To celebrate, here’s Slothbot!

[ NSF ]

This is one of those simple-seeming tasks that are really difficult for robots.

I love self-resetting training environments.

[ MIT CSAIL ]

The Chiel lab collaborates with engineers at the Center for Biologically Inspired Robotics Research at Case Western Reserve University to design novel worm-like robots that have potential applications in search-and-rescue missions, endoscopic medicine, or other scenarios requiring navigation through narrow spaces.

[ Case Western ]

ANYbotics partnered with Losinger Marazzi to explore ANYmal’s potential of patrolling construction sites to identify and report safety issues. With such a complex environment, only a robot designed to navigate difficult terrain is able to bring digitalization to such a physically demanding industry.

[ ANYbotics ]

Happy 2018 Halloween from Clearpath Robotics!

[ Clearpath ]

Overcoming illumination variance is a critical factor in vision-based navigation. Existing methods tackled this radical illumination variance issue by proposing camera control or high dynamic range (HDR) image fusion. Despite these efforts, we have found that the vision-based approaches still suffer from overcoming darkness. This paper presents real-time image synthesizing from carefully controlled seed low dynamic range (LDR) image, to enable visual simultaneous localization and mapping (SLAM) in an extremely dark environment (less than 10 lux).

[ KAIST ]

What can MoveIt do? Who knows! Let's find out!

[ MoveIt ]

Thanks Dave!

Here we pick a cube from a starting point, manipulate it within the hand, and then put it back. To explore the capabilities of the hand, no sensors were used in this demonstration. The RBO Hand 3 uses soft pneumatic actuators made of silicone. The softness imparts considerable robustness against variations in object pose and size. This lets us design manipulation funnels that work reliably without needing sensor feedback. We take advantage of this reliability to chain these funnels into more complex multi-step manipulation plans.

[ TU Berlin ]

If this was a real solar array, King Louie would have totally cleaned it. Mostly.

[ BYU ]

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, were demonstrated to have low efficiency, due to the lack of optimality consideration, conservative motion plans and low decision frequencies. In this paper, we propose FUEL, a hierarchical framework that can support Fast UAV ExpLoration in complex unknown environments.

[ HKUST ]

Countless precise repetitions? This is the perfect task for a robot, thought researchers at the University of Liverpool in the Department of Chemistry, and without further ado they developed an automation solution that can carry out and monitor research tasks, making autonomous decisions about what to do next.

[ Kuka ]

This video shows a demonstration of central results of the SecondHands project. In the context of maintenance and repair tasks, in warehouse environments, the collaborative humanoid robot ARMAR-6 demonstrates a number of cognitive and sensorimotor abilities such as 1) recognition of the need of help based on speech, force, haptics and visual scene and action interpretation, 2) collaborative bimanual manipulation of large objects, 3) compliant mobile manipulation, 4) grasping known and unknown objects and tools, 5) human-robot interaction (object and tool handover) 6) natural dialog and 7) force predictive control.

[ SecondHands ]

In celebration of Ada Lovelace Day, Silicon Valley Robotics hosted a panel of Women in Robotics.

[ Robohub ]

As part of the upcoming virtual IROS conference, HEBI robotics is putting together a tutorial on robotics actuation. While I’m sure HEBI would like you to take a long look at their own actuators, we’ve been assured that no matter what kind of actuators you use, this tutorial will still be informative and useful.

[ YouTube ] via [ HEBI Robotics ]

Thanks Dave!

This week’s UMD Lockheed Martin Robotics Seminar comes from Julie Shah at MIT, on “Enhancing Human Capability with Intelligent Machine Teammates.”

Every team has top performers- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. In this talk I share recent work investigating effective ways to blend the unique decision-making strengths of humans and machines. I discuss the development of computational models that enable machines to efficiently infer the mental state of human teammates and thereby collaborate with people in richer, more flexible ways.

[ UMD ]

Matthew Piccoli gives a talk to the UPenn GRASP Lab on “Trading Complexities: Smart Motors and Dumb Vehicles.”

We will discuss my research journey through Penn making the world's smallest, simplest flying vehicles, and in parallel making the most complex brushless motors. What do they have in common? We'll touch on why the quadrotor went from an obscure type of helicopter to the current ubiquitous drone. Finally, we'll get into my life after Penn and what tools I'm creating to further drone and robot designs of the future.

[ UPenn ] Continue reading →

Posted in Human Robots

#437592 Coordinated Robotics Wins DARPA SubT ...

Posted on November 20, 2020 by Android

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

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

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

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

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

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

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

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

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

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

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

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

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

How will you be preparing for the SubT Final?

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

Image: DARPA

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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