Tag Archives: goal

#437635 Toyota Research Demonstrates ...

Posted on November 22, 2020 by Android

Over the last several years, Toyota has been putting more muscle into forward-looking robotics research than just about anyone. In addition to the Toyota Research Institute (TRI), there’s that massive 175-acre robot-powered city of the future that Toyota still plans to build next to Mount Fuji. Even Toyota itself acknowledges that it might be crazy, but that’s just how they roll—as TRI CEO Gill Pratt told me a while back, when Toyota decides to do something, they really do go all-in on it.

TRI has been focusing heavily on home robots, which is reflective of the long-term nature of what TRI is trying to do, because home robots are both the place where we’ll need robots the most at the same time as they’re the place where it’s going to be hardest to deploy them. The unpredictable nature of homes, and the fact that homes tend to have squishy fragile people in them, are robot-unfriendly characteristics, but as the population continues to age (an increasingly acute problem in Japan), homes offer an enormous amount of potential for helping us maintain our independence.

Today, Toyota is showing off some of the research that it’s been working on recently, in the form of a virtual reality presentation in lieu of an in-person press event. For journalists, TRI pre-loaded the recording onto a VR headset, which was FedEx’ed to my house. You can watch the entire 40-minute presentation in 360 video on YouTube (or in VR if you have a headset of your own), but if you don’t watch the whole thing, you should at least check out the full-on GLaDOS (with arms) that TRI thinks belongs in your home.

The presentation features an introduction from Gill Pratt, who looks entirely too comfortable embedded inside of one of TRI’s telepresence robots. The event also covers a lot of territory, but the highlight is almost certainly the new hardware that TRI demonstrates.

Soft bubble gripper

Photo: TRI

This is a “soft bubble gripper,” under development at TRI’s Cambridge, Mass., branch. These passively-compliant, air-filled grippers make it easier to grasp many different kinds of objects safely, but the nifty thing is that they’ve got cameras inside of them watching a pattern of dots on the interior of the soft membrane.

When the outside of the bubble makes contact with an object, the bubble deforms, and the deformation of the dot pattern on the inside can be tracked by the camera to determine both directions and magnitudes of forces. This is a concept that we’ve seen elsewhere before, but TRI’s implementation is a clever way of making an inherently safe end effector that can still perform all the sensing you need it to do for relatively complex manipulation tasks.

The bubble gripper was presented at ICRA this year, and you can read the technical paper here.

Ceiling-mounted home robot

Photo: TRI

I don’t know whether robots dangling from the ceiling was somehow sinister pre-Portal, but it sure as heck is for me having played through that game a couple of times, and it’s since been reinforced by AUTO from WALL-E.

The reason that we generally see robots mounted on the floor or on tables or on mobile bases is that we’re bipeds, not bats, and giving a robot access to a human-like workspace is easiest to do if you also give that robot a human-like position and orientation. And if you want to be able to reach stuff high up, you do what TRI did with their previous generation of kitchen manipulator, and just give it the ability to make itself super tall. But TRI is convinced it’s a good place to put our future home robots:

One innovative concept is a “gantry robot” that would descend from an overhead framework to perform tasks such as loading the dishwasher, wiping surfaces, and clearing clutter. By traveling on the ceiling, the robot avoids the problems of navigating household floor clutter and navigating cramped spaces. When not in use, the robot would tuck itself up out of the way. To further investigate this idea, the team has built a laboratory prototype robot that can do all the same tasks as a floor-based mobile robot but with the innovative overhead mobility system.

Another obvious problem with the gantry robot is that you have to install all kinds of stuff in your ceiling for this to work, which makes it very impractical (if not totally impossible) to introduce a system like this into a home that wasn’t built specifically for it. If, however, you do build a home with a robot like this in mind, the animation below from TRI shows how it could be extra useful. Suddenly, stairs are a non-issue. Payload is presumably also a non-issue, since loads can be transferred to the ceiling. Batteries become unnecessary, so the whole robot can be much lighter weight, which in turn makes it safer. Sensors get a fantastic view, and obstacle avoidance becomes trivial.

Robots as “time machines”

Photo: TRI

TRI’s presentation covered more than what we’ve highlighted here—our focus has been on the hardware prototypes, but TRI had more to talk about, including learning through demonstration, scaling learning through simulation, and how TRI has been working with users to figure out what research directions should be explored. It’s all available right now on YouTube, and it’s well worth 40 minutes of your time.

“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings”
—Gill Pratt, TRI

It’s only been five years since Toyota announced the $1 billion investment that established TRI, and it feels like the progress that’s been made since then has been substantial. It’s not often that vision, resources, and long-term commitment come together like this, and TRI’s emphasis on making life better for people is one of the things that helps to keep us optimistic about the future of robotics.

“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings,” Gill Pratt told us. “And what it means to amplify a person, particularly as they’re aging—what we’re really trying to do is build a time machine. This may sound fanciful, and of course we can’t build a real time machine, but maybe we can build robotic assistants to make our lives as we age seem as if we are actually using a time machine.” He explains that it doesn’t mean building robots for convenience or to do our jobs for us. “It means building technology that enables us to continue to live and to work and to relate to each other as if we were younger,” he says. “And that’s really what our main goal is.” Continue reading →

Posted in Human Robots

#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

#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

#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.

[ DARPA SubT ] Continue reading →

Posted in Human Robots

#437579 Disney Research Makes Robotic Gaze ...

Posted on November 19, 2020 by Android

While it’s not totally clear to what extent human-like robots are better than conventional robots for most applications, one area I’m personally comfortable with them is entertainment. The folks over at Disney Research, who are all about entertainment, have been working on this sort of thing for a very long time, and some of their animatronic attractions are actually quite impressive.

The next step for Disney is to make its animatronic figures, which currently feature scripted behaviors, to perform in an interactive manner with visitors. The challenge is that this is where you start to get into potential Uncanny Valley territory, which is what happens when you try to create “the illusion of life,” which is what Disney (they explicitly say) is trying to do.

In a paper presented at IROS this month, a team from Disney Research, Caltech, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering is trying to nail that illusion of life with a single, and perhaps most important, social cue: eye gaze.

Before you watch this video, keep in mind that you’re watching a specific character, as Disney describes:

The robot character plays an elderly man reading a book, perhaps in a library or on a park bench. He has difficulty hearing and his eyesight is in decline. Even so, he is constantly distracted from reading by people passing by or coming up to greet him. Most times, he glances at people moving quickly in the distance, but as people encroach into his personal space, he will stare with disapproval for the interruption, or provide those that are familiar to him with friendly acknowledgment.

What, exactly, does “lifelike” mean in the context of robotic gaze? The paper abstract describes the goal as “[seeking] to create an interaction which demonstrates the illusion of life.” I suppose you could think of it like a sort of old-fashioned Turing test focused on gaze: If the gaze of this robot cannot be distinguished from the gaze of a human, then victory, that’s lifelike. And critically, we’re talking about mutual gaze here—not just a robot gazing off into the distance, but you looking deep into the eyes of this robot and it looking right back at you just like a human would. Or, just like some humans would.

The approach that Disney is using is more animation-y than biology-y or psychology-y. In other words, they’re not trying to figure out what’s going on in our brains to make our eyes move the way that they do when we’re looking at other people and basing their control system on that, but instead, Disney just wants it to look right. This “visual appeal” approach is totally fine, and there’s been an enormous amount of human-robot interaction (HRI) research behind it already, albeit usually with less explicitly human-like platforms. And speaking of human-like platforms, the hardware is a “custom Walt Disney Imagineering Audio-Animatronics bust,” which has DoFs that include neck, eyes, eyelids, and eyebrows.

In order to decide on gaze motions, the system first identifies a person to target with its attention using an RGB-D camera. If more than one person is visible, the system calculates a curiosity score for each, currently simplified to be based on how much motion it sees. Depending on which person that the robot can see has the highest curiosity score, the system will choose from a variety of high level gaze behavior states, including:

Read: The Read state can be considered the “default” state of the character. When not executing another state, the robot character will return to the Read state. Here, the character will appear to read a book located at torso level.

Glance: A transition to the Glance state from the Read or Engage states occurs when the attention engine indicates that there is a stimuli with a curiosity score […] above a certain threshold.

Engage: The Engage state occurs when the attention engine indicates that there is a stimuli […] to meet a threshold and can be triggered from both Read and Glance states. This state causes the robot to gaze at the person-of-interest with both the eyes and head.

Acknowledge: The Acknowledge state is triggered from either Engage or Glance states when the person-of-interest is deemed to be familiar to the robot.

Running underneath these higher level behavior states are lower level motion behaviors like breathing, small head movements, eye blinking, and saccades (the quick eye movements that occur when people, or robots, look between two different focal points). The term for this hierarchical behavioral state layering is a subsumption architecture, which goes all the way back to Rodney Brooks’ work on robots like Genghis in the 1980s and Cog and Kismet in the ’90s, and it provides a way for more complex behaviors to emerge from a set of simple, decentralized low-level behaviors.

“25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”
—Rodney Brooks, MIT emeritus professor

Brooks, an emeritus professor at MIT and, most recently, cofounder and CTO of Robust.ai, tweeted about the Disney project, saying: “People underestimate how long it takes to get from academic paper to real world robotics. 25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”

From the paper:

Although originally intended for control of mobile robots, we find that the subsumption architecture, as presented in [17], lends itself as a framework for organizing animatronic behaviors. This is due to the analogous use of subsumption in human behavior: human psychomotor behavior can be intuitively modeled as layered behaviors with incoming sensory inputs, where higher behavioral levels are able to subsume lower behaviors. At the lowest level, we have involuntary movements such as heartbeats, breathing and blinking. However, higher behavioral responses can take over and control lower level behaviors, e.g., fight-or-flight response can induce faster heart rate and breathing. As our robot character is modeled after human morphology, mimicking biological behaviors through the use of a bottom-up approach is straightforward.

The result, as the video shows, appears to be quite good, although it’s hard to tell how it would all come together if the robot had more of, you know, a face. But it seems like you don’t necessarily need to have a lifelike humanoid robot to take advantage of this architecture in an HRI context—any robot that wants to make a gaze-based connection with a human could benefit from doing it in a more human-like way.

“Realistic and Interactive Robot Gaze,” by Matthew K.X.J. Pan, Sungjoon Choi, James Kennedy, Kyna McIntosh, Daniel Campos Zamora, Gunter Niemeyer, Joohyung Kim, Alexis Wieland, and David Christensen from Disney Research, California Institute of Technology, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering, was presented at IROS 2020. You can find the full paper, along with a 13-minute video presentation, on the IROS on-demand conference website.

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