Tag Archives: training

#439012 Video Friday: Man-Machine Synergy ...

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!):

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Man-Machine Synergy Effectors, Inc. is a Japanese company working on an absolutely massive “human machine synergistic effect device,” which is a huge robot controlled by a nearby human using a haptic rig.

From the look of things, the next generation will be able to move around. Whoa.

[ MMSE ]

This method of loading and unloading AMRs without having them ever stop moving is so obvious that there must be some equally obvious reason why I've never seen it done in practice.

The LoadRunner is able to transport and sort parcels weighing up to 30 kilograms. This makes it the perfect luggage carrier for airports. These AI-driven go-carts can also work in concert as larger collectives to carry large, heavy and bulky objects. Every LoadRunner can also haul up to four passive trailers. Powered by four electric motors, the LoadRunner sharply brakes at just the right moment right in front of its destination and the payload slides from the robot onto the delivery platform.

[ Fraunhofer ] via [ Gizmodo ]

Ayato Kanada at Kyushu University wrote in to share this clever “dislocatable joint,” a way of combining continuum and rigid robots.

[ Paper ]

Thanks Ayato!

The DodgeDrone challenge revisits the popular dodgeball game in the context of autonomous drones. Specifically, participants will have to code navigation policies to fly drones between waypoints while avoiding dynamic obstacles. Drones are fast but fragile systems: as soon as something hits them, they will crash! Since objects will move towards the drone with different speeds and acceleration, smart algorithms are required to avoid them!

This could totally happen in real life, and we need to be prepared for it!

[ DodgeDrone Challenge ]

In addition to winning the Best Student Design Competition CREATIVITY Award at HRI 2021, this paper would also have won the Best Paper Title award, if that award existed.

[ Paper ]

Robots are traditionally bound by a fixed morphology during their operational lifetime, which is limited to adapting only their control strategies. Here we present the first quadrupedal robot that can morphologically adapt to different environmental conditions in outdoor, unstructured environments.

We show that the robot exploits its training to effectively transition between different morphological configurations, exhibiting substantial performance improvements over a non-adaptive approach. The demonstrated benefits of real-world morphological adaptation demonstrate the potential for a new embodied way of incorporating adaptation into future robotic designs.

[ Nature ]

A drone video shot in a Minneapolis bowling alley was hailed as an instant classic. One Hollywood veteran said it “adds to the language and vocabulary of cinema.” One IEEE Spectrum editor said “hey that's pretty cool.”

[ Bryant Lake Bowl ]

It doesn't take a robot to convince me to buy candy, but I think if I buy candy from Relay it's a business expense, right?

[ RIS ]

DARPA is making progress on its AI dogfighting program, with physical flight tests expected this year.

[ DARPA ACE ]

Unitree Robotics has realized that the Empire needs to be overthrown!

[ Unitree ]

Windhover Labs, an emerging leader in open and reliable flight software and hardware, announces the upcoming availability of its first hardware product, a low cost modular flight computer for commercial drones and small satellites.

[ Windhover ]

As robots and autonomous systems are poised to become part of our everyday lives, the University of Michigan and Ford are opening a one-of-a-kind facility where they’ll develop robots and roboticists that help make lives better, keep people safer and build a more equitable society.

[ U Michigan ]

The adaptive robot Rizon combined with a new hybrid electrostatic and gecko-inspired gripping pad developed by Stanford BDML can manipulate bulky, non-smooth items in the most effort-saving way, which broadens the applications in retail and household environments.

[ Flexiv ]

Thanks Yunfan!

I don't know why anyone would want things to get MORE icy, but if you do for some reason, you can make it happen with a Husky.

Is winter over yet?

[ Clearpath ]

Skip ahead to about 1:20 to see a pair of Gita robots following a Spot following a human like a chain of lil’ robot duckings.

[ PFF ]

Here are a couple of retro robotics videos, one showing teleoperated humanoids from 2000, and the other showing a robotic guide dog from 1976 (!)

[ Tachi Lab ]

Thanks Fan!

If you missed Chad Jenkins' talk “That Ain’t Right: AI Mistakes and Black Lives” last time, here's another opportunity to watch from Robotics Today, and it includes a top notch panel discussion at the end.

[ Robotics Today ]

Since its founding in 1979, the Robotics Institute (RI) at Carnegie Mellon University has been leading the world in robotics research and education. In the mid 1990s, RI created NREC as the applied R&D center within the Institute with a specific mission to apply robotics technology in an impactful way on real-world applications. In this talk, I will go over numerous R&D programs that I have led at NREC in the past 25 years.

[ CMU ] Continue reading

Posted in Human Robots

#437445 Humanoid Robotic Barista

The “Robotic Coffee Master” by OrionStar is a robot coffee brewing guru that uses expert brewing techniques with millimeter-level stability and accuracy to make the perfect cuppa every time. This robotic barista incorporates 3,000 hours of A.I. learning, 30,000 hours … Continue reading

Posted in Human Robots

#438807 Visible Touch: How Cameras Can Help ...

The dawn of the robot revolution is already here, and it is not the dystopian nightmare we imagined. Instead, it comes in the form of social robots: Autonomous robots in homes and schools, offices and public spaces, able to interact with humans and other robots in a socially acceptable, human-perceptible way to resolve tasks related to core human needs.

To design social robots that “understand” humans, robotics scientists are delving into the psychology of human communication. Researchers from Cornell University posit that embedding the sense of touch in social robots could teach them to detect physical interactions and gestures. They describe a way of doing so by relying not on touch but on vision.

A USB camera inside the robot captures shadows of hand gestures on the robot’s surface and classifies them with machine-learning software. They call this method ShadowSense, which they define as a modality between vision and touch, bringing “the high resolution and low cost of vision-sensing to the close-up sensory experience of touch.”

Touch-sensing in social or interactive robots is usually achieved with force sensors or capacitive sensors, says study co-author Guy Hoffman of the Sibley School of Mechanical and Aerospace Engineering at Cornell University. The drawback to his group’s approach has been that, even to achieve coarse spatial resolution, many sensors are needed in a small area.

However, working with non-rigid, inflatable robots, Hoffman and his co-researchers installed a consumer-grade USB camera to which they attached a fisheye lens for a wider field of vision.

“Given that the robot is already hollow, and has a soft and translucent skin, we could do touch interaction by looking at the shadows created by people touching the robot,” says Hoffman. They used deep neural networks to interpret the shadows. “And we were able to do it with very high accuracy,” he says. The robot was able to interpret six different gestures, including one- or two-handed touch, pointing, hugging and punching, with an accuracy of 87.5 to 96 percent, depending on the lighting.

This is not the first time that computer vision has been used for tactile sensing, though the scale and application of ShadowSense is unique. “Photography has been used for touch mainly in robotic grasping,” says Hoffman. By contrast, Hoffman and collaborators wanted to develop a sense that could be “felt” across the whole of the device.

The potential applications for ShadowSense include mobile robot guidance using touch, and interactive screens on soft robots. A third concerns privacy, especially in home-based social robots. “We have another paper currently under review that looks specifically at the ability to detect gestures that are further away [from the robot’s skin],” says Hoffman. This way, users would be able to cover their robot’s camera with a translucent material and still allow it to interpret actions and gestures from shadows. Thus, even though it’s prevented from capturing a high-resolution image of the user or their surrounding environment, using the right kind of training datasets, the robot can continue to monitor some kinds of non-tactile activities.

In its current iteration, Hoffman says, ShadowSense doesn’t do well in low-light conditions. Environmental noise, or shadows from surrounding objects, also interfere with image classification. Relying on one camera also means a single point of failure. “I think if this were to become a commercial product, we would probably [have to] work a little bit better on image detection,” says Hoffman.

As it was, the researchers used transfer learning—reusing a pre-trained deep-learning model in a new problem—for image analysis. “One of the problems with multi-layered neural networks is that you need a lot of training data to make accurate predictions,” says Hoffman. “Obviously, we don’t have millions of examples of people touching a hollow, inflatable robot. But we can use pre-trained networks trained on general images, which we have billions of, and we only retrain the last layers of the network using our own dataset.” Continue reading

Posted in Human Robots

#438801 This AI Thrashes the Hardest Atari Games ...

Learning from rewards seems like the simplest thing. I make coffee, I sip coffee, I’m happy. My brain registers “brewing coffee” as an action that leads to a reward.

That’s the guiding insight behind deep reinforcement learning, a family of algorithms that famously smashed most of Atari’s gaming catalog and triumphed over humans in strategy games like Go. Here, an AI “agent” explores the game, trying out different actions and registering ones that let it win.

Except it’s not that simple. “Brewing coffee” isn’t one action; it’s a series of actions spanning several minutes, where you’re only rewarded at the very end. By just tasting the final product, how do you learn to fine-tune grind coarseness, water to coffee ratio, brewing temperature, and a gazillion other factors that result in the reward—tasty, perk-me-up coffee?

That’s the problem with “sparse rewards,” which are ironically very abundant in our messy, complex world. We don’t immediately get feedback from our actions—no video-game-style dings or points for just grinding coffee beans—yet somehow we’re able to learn and perform an entire sequence of arm and hand movements while half-asleep.

This week, researchers from UberAI and OpenAI teamed up to bestow this talent on AI.

The trick is to encourage AI agents to “return” to a previous step, one that’s promising for a winning solution. The agent then keeps a record of that state, reloads it, and branches out again to intentionally explore other solutions that may have been left behind on the first go-around. Video gamers are likely familiar with this idea: live, die, reload a saved point, try something else, repeat for a perfect run-through.

The new family of algorithms, appropriately dubbed “Go-Explore,” smashed notoriously difficult Atari games like Montezuma’s Revenge that were previously unsolvable by its AI predecessors, while trouncing human performance along the way.

It’s not just games and digital fun. In a computer simulation of a robotic arm, the team found that installing Go-Explore as its “brain” allowed it to solve a challenging series of actions when given very sparse rewards. Because the overarching idea is so simple, the authors say, it can be adapted and expanded to other real-world problems, such as drug design or language learning.

Growing Pains
How do you reward an algorithm?

Rewards are very hard to craft, the authors say. Take the problem of asking a robot to go to a fridge. A sparse reward will only give the robot “happy points” if it reaches its destination, which is similar to asking a baby, with no concept of space and danger, to crawl through a potential minefield of toys and other obstacles towards a fridge.

“In practice, reinforcement learning works very well, if you have very rich feedback, if you can tell, ‘hey, this move is good, that move is bad, this move is good, that move is bad,’” said study author Joost Huinzinga. However, in situations that offer very little feedback, “rewards can intentionally lead to a dead end. Randomly exploring the space just doesn’t cut it.”

The other extreme is providing denser rewards. In the same robot-to-fridge example, you could frequently reward the bot as it goes along its journey, essentially helping “map out” the exact recipe to success. But that’s troubling as well. Over-holding an AI’s hand could result in an extremely rigid robot that ignores new additions to its path—a pet, for example—leading to dangerous situations. It’s a deceptive AI solution that seems effective in a simple environment, but crashes in the real world.

What we need are AI agents that can tackle both problems, the team said.

Intelligent Exploration
The key is to return to the past.

For AI, motivation usually comes from “exploring new or unusual situations,” said Huizinga. It’s efficient, but comes with significant downsides. For one, the AI agent could prematurely stop going back to promising areas because it thinks it had already found a good solution. For another, it could simply forget a previous decision point because of the mechanics of how it probes the next step in a problem.

For a complex task, the end result is an AI that randomly stumbles around towards a solution while ignoring potentially better ones.

“Detaching from a place that was previously visited after collecting a reward doesn’t work in difficult games, because you might leave out important clues,” Huinzinga explained.

Go-Explore solves these problems with a simple principle: first return, then explore. In essence, the algorithm saves different approaches it previously tried and loads promising save points—once more likely to lead to victory—to explore further.

Digging a bit deeper, the AI stores screen caps from a game. It then analyzes saved points and groups images that look alike as a potential promising “save point” to return to. Rinse and repeat. The AI tries to maximize its final score in the game, and updates its save points when it achieves a new record score. Because Atari doesn’t usually allow people to revisit any random point, the team used an emulator, which is a kind of software that mimics the Atari system but with custom abilities such as saving and reloading at any time.

The trick worked like magic. When pitted against 55 Atari games in the OpenAI gym, now commonly used to benchmark reinforcement learning algorithms, Go-Explore knocked out state-of-the-art AI competitors over 85 percent of the time.

It also crushed games previously unbeatable by AI. Montezuma’s Revenge, for example, requires you to move Pedro, the blocky protagonist, through a labyrinth of underground temples while evading obstacles such as traps and enemies and gathering jewels. One bad jump could derail the path to the next level. It’s a perfect example of sparse rewards: you need a series of good actions to get to the reward—advancing onward.

Go-Explore didn’t just beat all levels of the game, a first for AI. It also scored higher than any previous record for reinforcement learning algorithms at lower levels while toppling the human world record.

Outside a gaming environment, Go-Explore was also able to boost the performance of a simulated robot arm. While it’s easy for humans to follow high-level guidance like “put the cup on this shelf in a cupboard,” robots often need explicit training—from grasping the cup to recognizing a cupboard, moving towards it while avoiding obstacles, and learning motions to not smash the cup when putting it down.

Here, similar to the real world, the digital robot arm was only rewarded when it placed the cup onto the correct shelf, out of four possible shelves. When pitted against another algorithm, Go-Explore quickly figured out the movements needed to place the cup, while its competitor struggled with even reliably picking the cup up.

Combining Forces
By itself, the “first return, then explore” idea behind Go-Explore is already powerful. The team thinks it can do even better.

One idea is to change the mechanics of save points. Rather than reloading saved states through the emulator, it’s possible to train a neural network to do the same, without needing to relaunch a saved state. It’s a potential way to make the AI even smarter, the team said, because it can “learn” to overcome one obstacle once, instead of solving the same problem again and again. The downside? It’s much more computationally intensive.

Another idea is to combine Go-Explore with an alternative form of learning, called “imitation learning.” Here, an AI observes human behavior and mimics it through a series of actions. Combined with Go-Explore, said study author Adrien Ecoffet, this could make more robust robots capable of handling all the complexity and messiness in the real world.

To the team, the implications go far beyond Go-Explore. The concept of “first return, then explore” seems to be especially powerful, suggesting “it may be a fundamental feature of learning in general.” The team said, “Harnessing these insights…may be essential…to create generally intelligent agents.”

Image Credit: Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, and Jeff Clune Continue reading

Posted in Human Robots

#438785 Video Friday: A Blimp For Your Cat

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!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

Shiny robotic cat toy blimp!

I am pretty sure this is Google Translate getting things wrong, but the About page mentions that the blimp will “take you to your destination after appearing in the death of God.”

[ NTT DoCoMo ] via [ RobotStart ]

If you have yet to see this real-time video of Perseverance landing on Mars, drop everything and watch it.

During the press conference, someone commented that this is the first time anyone on the team who designed and built this system has ever seen it in operation, since it could only be tested at the component scale on Earth. This landing system has blown my mind since Curiosity.

Here's a better look at where Percy ended up:

[ NASA ]

The fact that Digit can just walk up and down wet, slippery, muddy hills without breaking a sweat is (still) astonishing.

[ Agility Robotics ]

SkyMul wants drones to take over the task of tying rebar, which looks like just the sort of thing we'd rather robots be doing so that we don't have to:

The tech certainly looks promising, and SkyMul says that they're looking for some additional support to bring things to the pilot stage.

[ SkyMul ]

Thanks Eohan!

Flatcat is a pet-like, playful robot that reacts to touch. Flatcat feels everything exactly: Cuddle with it, romp around with it, or just watch it do weird things of its own accord. We are sure that flatcat will amaze you, like us, and caress your soul.

I don't totally understand it, but I want it anyway.

[ Flatcat ]

Thanks Oswald!

This is how I would have a romantic dinner date if I couldn't get together in person. Herman the UR3 and an OptiTrack system let me remotely make a romantic meal!

[ Dave's Armoury ]

Here, we propose a novel design of deformable propellers inspired by dragonfly wings. The structure of these propellers includes a flexible segment similar to the nodus on a dragonfly wing. This flexible segment can bend, twist and even fold upon collision, absorbing force upon impact and protecting the propeller from damage.

[ Paper ]

Thanks Van!

In the 1970s, The CIA​ created the world's first miniaturized unmanned aerial vehicle, or UAV, which was intended to be a clandestine listening device. The Insectothopter was never deployed operationally, but was still revolutionary for its time.

It may never have been deployed (not that they'll admit to, anyway), but it was definitely operational and could fly controllably.

[ CIA ]

Research labs are starting to get Digits, which means we're going to get a much better idea of what its limitations are.

[ Ohio State ]

This video shows the latest achievements for LOLA walking on undetected uneven terrain. The robot is technically blind, not using any camera-based or prior information on the terrain.

[ TUM ]

We define “robotic contact juggling” to be the purposeful control of the motion of a three-dimensional smooth object as it rolls freely on a motion-controlled robot manipulator, or “hand.” While specific examples of robotic contact juggling have been studied before, in this paper we provide the first general formulation and solution method for the case of an arbitrary smooth object in single-point rolling contact on an arbitrary smooth hand.

[ Paper ]

Thanks Fan!

A couple of new cobots from ABB, designed to work safely around humans.

[ ABB ]

Thanks Fan!

It's worth watching at least a little bit of Adam Savage testing Spot's new arm, because we get to see Spot try, fail, and eventually succeed at an autonomous door-opening behavior at the 10 minute mark.

[ Tested ]

SVR discusses diversity with guest speakers Dr. Michelle Johnson from the GRASP Lab at UPenn; Dr Ariel Anders from Women in Robotics and first technical hire at Robust.ai; Alka Roy from The Responsible Innovation Project; and Kenechukwu C. Mbanesi and Kenya Andrews from Black in Robotics. The discussion here is moderated by Dr. Ken Goldberg—artist, roboticist and Director of the CITRIS People and Robots Lab—and Andra Keay from Silicon Valley Robotics.

[ SVR ]

RAS presents a Soft Robotics Debate on Bioinspired vs. Biohybrid Design.

In this debate, we will bring together experts in Bioinspiration and Biohybrid design to discuss the necessary steps to make more competent soft robots. We will try to answer whether bioinspired research should focus more on developing new bioinspired material and structures or on the integration of living and artificial structures in biohybrid designs.

[ RAS SoRo ]

IFRR presents a Colloquium on Human Robot Interaction.

Across many application domains, robots are expected to work in human environments, side by side with people. The users will vary substantially in background, training, physical and cognitive abilities, and readiness to adopt technology. Robotic products are expected to not only be intuitive, easy to use, and responsive to the needs and states of their users, but they must also be designed with these differences in mind, making human-robot interaction (HRI) a key area of research.

[ IFRR ]

Vijay Kumar, Nemirovsky Family Dean and Professor at Penn Engineering, gives an introduction to ENIAC day and David Patterson, Pardee Professor of Computer Science, Emeritus at the University of California at Berkeley, speaks about the legacy of the ENIAC and its impact on computer architecture today. This video is comprised of lectures one and two of nine total lectures in the ENIAC Day series.

There are more interesting ENIAC videos at the link below, but we'll highlight this particular one, about the women of the ENIAC, also known as the First Programmers.

[ ENIAC Day ] Continue reading

Posted in Human Robots