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#439836 Video Friday: Dusty at Work

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. 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!):

ROSCon 2021 – October 20-21, 2021 – [Online Event]Silicon Valley Robot Block Party – October 23, 2021 – Oakland, CA, USALet us know if you have suggestions for next week, and enjoy today's videos.
I love watching Dusty Robotics' field printer at work. I don't know whether it's intentional or not, but it's go so much personality somehow.

[ Dusty Robotics ]
A busy commuter is ready to walk out the door, only to realize they've misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys. Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.
While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn't quite fast enough yet for these uses.[ MIT ]
CSIRO Data61 had, I'm pretty sure, the most massive robots in the entire SubT competition. And this is how you solve doors with a massive robot.

[ CSIRO ]
You know how robots are supposed to be doing things that are too dangerous for humans? I think sailing through a hurricane qualifies..

This second video, also captured by this poor Saildrone, is if anything even worse:

[ Saildrone ] via [ NOAA ]
Soft Robotics can handle my taquitos anytime.

[ Soft Robotics ]
This is brilliant, if likely unaffordable for most people.

[ Eric Paulos ]
I do not understand this robot at all, nor can I tell whether it's friendly or potentially dangerous or both.

[ Keunwook Kim ]
This sort of thing really shouldn't have to exist for social home robots, but I'm glad it does, I guess?

It costs $100, though.
[ Digital Dream Labs ]
If you watch this video closely, you'll see that whenever a simulated ANYmal falls over, it vanishes from existence. This is a new technique for teaching robots to walk by threatening them with extinction if they fail.

But seriously how do I get this as a screensaver?
[ RSL ]
Zimbabwe Flying Labs' Tawanda Chihambakwe shares how Zimbabwe Flying Labs got their start, using drones for STEM programs, and how drones impact conservation and agriculture.
[ Zimbabwe Flying Labs ]
DARPA thoughtfully provides a video tour of the location of every artifact on the SubT Final prize course. Some of them are hidden extraordinarily well.

Also posted by DARPA this week are full prize round run videos for every team; here are the top three: MARBLE, CSIRO Data61, and CERBERUS.

[ DARPA SubT ]
An ICRA 2021 plenary talk from Fumihito Arai at the University of Tokyo, on “Robotics and Automation in Micro & Nano-Scales.”
[ ICRA 2021 ]
This week's UPenn GRASP Lab Seminar comes from Rahul Mangharam, on “What can we learn from Autonomous Racing?”

[ UPenn ] Continue reading

Posted in Human Robots

#439826 Autonomous Racing Drones Dodge Through ...

It seems inevitable that sooner or later, the performance of autonomous drones will
surpass the performance of even the best human pilots. Usually things in robotics that seem inevitable happen later as opposed to sooner, but drone technology seems to be the exception to this. We've seen an astonishing amount of progress over the past few years, even to the extent of sophisticated autonomy making it into the hands of consumers at an affordable price.

The cutting edge of drone research right now is putting drones with relatively simple onboard sensing and computing in situations that require fast and highly aggressive maneuvers. In a paper
published yesterday in Science Robotics, roboticists from Davide Scaramuzza's Robotics and Perception Group at the University of Zurich along with partners at Intel demonstrate a small, self-contained, fully autonomous drone that can aggressively fly through complex environments at speeds of up to 40kph.

The trick here, to the extent that there's a trick, is that the drone performs a direct mapping of sensor input (from an Intel RealSense 435 stereo depth camera) to collision-free trajectories. Conventional obstacle avoidance involves first collecting sensor data; making a map based on that sensor data; and finally making a plan based on that map. This approach works perfectly fine as long as you're not concerned with getting all of that done quickly, but for a drone with limited onboard resources moving at high speed, it just takes too long. UZH's approach is instead to go straight from sensor input to trajectory output, which is much faster and allows the speed of the drone to increase substantially.

The convolutional network that performs this sensor-to-trajectory mapping was trained entirely in simulation, which is cheaper and easier but (I would have to guess) less fun than letting actual drones hammer themselves against obstacles over and over until they figure things out. A simulated “expert” drone pilot that has access to a 3D point cloud, perfect state estimation, and computation that's not constrained by real-time requirements trains its own end-to-end policy, which is of course not achievable in real life. But then, the simulated system that will be operating under real-life constraints just learns in simulation to match the expert as closely as possible, which is how you get that expert-level performance in a way that can be taken out of simulation and transferred to a real drone without any adaptation or fine-tuning.

The other big part of this is making that sim-to-real transition, which can be problematic because simulation doesn't always do a great job of simulating everything that happens in the world that can screw with a robot. But this method turns out to be very robust against motion blur, sensor noise, and other perception artifacts. The drone has successfully navigated through real world environments including snowy terrains, derailed trains, ruins, thick vegetation, and collapsed buildings.

“While humans require years to train, the AI, leveraging high-performance simulators, can reach comparable navigation abilities much faster, basically overnight.” -Antonio Loquercio, UZH

This is not to say that the performance here is flawless—the system still has trouble with very low illumination conditions (because the cameras simply can't see), as well as similar vision challenges like dust, fog, glare, and transparent or reflective surfaces. The training also didn't include dynamic obstacles, although the researchers tell us that moving things shouldn't be a problem even now as long as their speed relative to the drone is negligible. Many of these problems could potentially be mitigated by using
event cameras rather than traditional cameras, since faster sensors, especially ones tuned to detect motion, would be ideal for high speed drones.

The researchers tell us that their system does not (yet) surpass the performance of expert humans in these challenging environments:

Analyzing their performance indicates that humans have a very rich and detailed understanding of their surroundings and are capable of planning and executing plans that span far in the future (our approach plans only one second into the future). Both are capabilities that today's autonomous systems still lack. We see our work as a stepping stone towards faster autonomous flight that is enabled by directly predicting collision-free trajectories from high-dimensional (noisy) sensory input.

This is one of the things that is likely coming next, though—giving the drone the ability to learn and improve from real-world experience. Coupled with more capable sensors and always increasing computer power, pushing that flight envelope past 40 kph in complex environments seems like it's not just possible, but inevitable. Continue reading

Posted in Human Robots

#439237 Agility Robotics’ Cassie Is Now ...

Bipedal robots are a huge hassle. They’re expensive, complicated, fragile, and they spend most of their time almost but not quite falling over. That said, bipeds are worth it because if you want a robot to go everywhere humans go, the conventional wisdom is that the best way to do so is to make robots that can walk on two legs like most humans do. And the most frequent, most annoying two-legged thing that humans do to get places? Going up and down stairs.

Stairs have been a challenge for robots of all kinds (bipeds, quadrupeds, tracked robots, you name it) since, well, forever. And usually, when we see bipeds going up or down stairs nowadays, it involves a lot of sensing, a lot of computation, and then a fairly brittle attempt that all too often ends in tears for whoever has to put that poor biped back together again.

You’d think that the solution to bipedal stair traversal would just involve better sensing and more computation to model the stairs and carefully plan footsteps. But an approach featured in upcoming Robotics Science and Systems conference paper from Oregon State University and Agility Robotics does away will all of that out and instead just throws a Cassie biped at random outdoor stairs with absolutely no sensing at all. And it works spectacularly well.

A couple of things to bear in mind: Cassie is “blind” in the sense that it has no information about the stairs that it’s going up or down. The robot does get proprioceptive feedback, meaning that it knows what kind of contact its limbs are making with the stairs. Also, the researchers do an admirable job of keeping that safety tether slack, and Cassie isn’t being helped by it in the least—it’s just there to prevent a catastrophic fall.

What really bakes my noodle about this video is how amazing Cassie is at being kind of terrible at stair traversal. The robot is a total klutz: it runs into railings, stubs its toes, slips off of steps, misses steps completely, and occasionally goes backwards. Amazingly, Cassie still manages not only to not fall, but also to keep going until it gets where it needs to be.

And this is why this research is so exciting—rather than try to develop some kind of perfect stair traversal system that relies on high quality sensing and a lot of computation to optimally handle stairs, this approach instead embraces real-world constraints while managing to achieve efficient performance that’s real-world robust, if perhaps not the most elegant.

The secret to Cassie’s stair mastery isn’t much of a secret at all, since there’s a paper about it on arXiv. The researchers used reinforcement learning to train a simulated Cassie on permutations of stairs based on typical city building codes, with sets of stairs up to eight individual steps. To transfer the learned stair-climbing strategies (referred to as policies) effectively from simulation to the real world, the simulation included a variety of disturbances designed to represent the kinds of things that are hard to simulate accurately. For example, Cassie had its simulated joints messed with, its simulated processing speed tweaked, and even the simulated ground friction was jittered around. So, even though the simulation couldn’t perfectly mimic real ground friction, randomly mixing things up ensures that the controller (the software telling the robot how to move) gains robustness to a much wider range of situations.

One peculiarity of using reinforcement learning to train a robot is that even if you come up with something that works really well, it’s sometimes unclear exactly why. You may have noticed in the first video that the researchers are only able to hypothesize about the reasons for the controller’s success, and we asked one of the authors, Kevin Green, to try and explain what’s going on:

“Deep reinforcement learning has similar issues that we are seeing in a lot of machine learning applications. It is hard to understand the reasoning for why a learned controller performs certain actions. Is it exploiting a quirk of your simulation or your reward function? Is it perhaps stuck in a local minima? Sometimes the reward function is not specific enough and the policy can exhibit strange, vestigial behaviors simply because they are not rewarded or penalized. On the other hand, a reward function can be too constraining and can lead to a policy which doesn’t fully explore the space of possible actions, limiting performance. We do our best to ensure our simulation is accurate and that our rewards are objective and descriptive. From there, we really act more like biomechanists, observing a functioning system for hints as to the strategies that it is using to be highly successful.”

One of the strategies that they observed, first author Jonah Siekmann told us, is that Cassie does better on stairs when it’s moving faster, which is a bit of a counterintuitive thing for robots generally:

“Because the robot is blind, it can choose very bad foot placements. If it tries to place its foot on the very corner of a stair and shift its weight to that foot, the resulting force pushes the robot back down the stairs. At walking speed, this isn’t much of an issue because the robot’s momentum can overcome brief moments where it is being pushed backwards. At low speeds, the momentum is not sufficient to overcome a bad foot placement, and it will keep getting knocked backwards down the stairs until it falls. At high speeds, the robot tends to skip steps which pushes the robot closer to (and sometimes over) its limits.”

These bad foot placements are what lead to some of Cassie’s more impressive feats, Siekmann says. “Some of the gnarlier descents, where Cassie skips a step or three and recovers, were especially surprising. The robot also tripped on ascent and recovered in one step a few times. The physics are complicated, so to see those accurate reactions embedded in the learned controller was exciting. We haven’t really seen that kind of robustness before.” In case you’re worried that all of that robustness is in video editing, here’s an uninterrupted video of ten stair ascents and ten stair descents, featuring plenty of gnarliness.

We asked the researchers whether Cassie is better at stairs than a blindfolded human would be. “It’s difficult to say,” Siekmann told us. “We’ve joked lots of times that Cassie is superhuman at stair climbing because in the process of filming these videos we have tripped going up the stairs ourselves while we’re focusing on the robot or on holding a camera.”

A robot being better than a human at a dynamic task like this is obviously a very high bar, but my guess is that most of us humans are actually less prepared for blind stair navigation than Cassie is, because Cassie was explicitly trained on stairs that were uneven: “a small amount of noise (± 1cm) is added to the rise and run of each step such that the stairs are never entirely uniform, to prevent the policy from deducing the precise dimensions of the stairs via proprioception and subsequently overfitting to perfectly uniform stairs.” Speaking as someone who just tried jogging up my stairs with my eyes closed in the name of science, I absolutely relied on the assumption that my stairs were uniform. And when humans can’t rely on assumptions like that, it screws us up, even if we have eyeballs equipped.

Like most robot-y things, Cassie is operating under some significant constraints here. If Cassie seems even stompier than it usually is, that’s because it’s using this specific stair controller which is optimized for stairs and stair-like things but not much else.

“When you train neural networks to act as controllers, over time the learning algorithm refines the network so that it maximizes the reward specific to the environment that it sees,” explains Green. “This means that by training on flights of stairs, we get a very different looking controller compared to training on flat ground.” Green says that the stair controller works fine on flat ground, it’s just less efficient (and noisier). They’re working on ways of integrating multiple gait controllers that the robot can call on depending on what it’s trying to do; conceivably this might involve some very simple perception system just to tell the robot “hey look, there are some stairs, better engage stair mode.”

The paper ends with the statement that “this work has demonstrated surprising capabilities for blind locomotion and leaves open the question of where the limits lie.” I’m certainly surprised at Cassie’s stair capabilities, and it’ll be exciting to see what other environments this technique can be applied to. If there are limits, I’m sure that Cassie is going to try and find them.

Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning, by Jonah Siekmann, Kevin Green, John Warila, Alan Fern, and Jonathan Hurst from Oregon State University and Agility Robotics, will be presented at RSS 2021 in July. Continue reading

Posted in Human Robots

#438754 TALOS Humanoid Robot in Scotland

Video of TALOS arriving at the University of Edinburgh, being unpacked, and activated.

Posted in Human Robots

#439110 Robotic Exoskeletons Could One Day Walk ...

Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.

Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.

One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.

Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.

Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.

Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.

According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.

In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”

In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .

Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.

However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading

Posted in Human Robots