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#436180 Bipedal Robot Cassie Cal Learns to ...
There’s no particular reason why knowing how to juggle would be a useful skill for a robot. Despite this, robots are frequently taught how to juggle things. Blind robots can juggle, humanoid robots can juggle, and even drones can juggle. Why? Because juggling is hard, man! You have to think about a bunch of different things at once, and also do a bunch of different things at once, which this particular human at least finds to be overly stressful. While juggling may not stress robots out, it does require carefully coordinated sensing and computing and actuation, which means that it’s as good a task as any (and a more entertaining task than most) for testing the capabilities of your system.
UC Berkeley’s Cassie Cal robot, which consists of two legs and what could be called a torso if you were feeling charitable, has just learned to juggle by bouncing a ball on what would be her head if she had one of those. The idea is that if Cassie can juggle while balancing at the same time, she’ll be better able to do other things that require dynamic multitasking, too. And if that doesn’t work out, she’ll still be able to join the circus.
Cassie’s juggling is assisted by an external motion capture system that tracks the location of the ball, but otherwise everything is autonomous. Cassie is able to juggle the ball by leaning forwards and backwards, left and right, and moving up and down. She does this while maintaining her own balance, which is the whole point of this research—successfully executing two dynamic behaviors that may sometimes be at odds with one another. The end goal here is not to make a better juggling robot, but rather to explore dynamic multitasking, a skill that robots will need in order to be successful in human environments.
This work is from the Hybrid Robotics Lab at UC Berkeley, led by Koushil Sreenath, and is being done by Katherine Poggensee, Albert Li, Daniel Sotsaikich, Bike Zhang, and Prasanth Kotaru.
For a bit more detail, we spoke with Albert Li via email.
Image: UC Berkeley
UC Berkeley’s Cassie Cal getting ready to juggle.
IEEE Spectrum: What would be involved in getting Cassie to juggle without relying on motion capture?
Albert Li: Our motivation for starting off with motion capture was to first address the control challenge of juggling on a biped without worrying about implementing the perception. We actually do have a ball detector working on a camera, which would mean we wouldn’t have to rely on the motion capture system. However, we need to mount the camera in a way that it would provide the best upwards field of view, and we also have develop a reliable estimator. The estimator is particularly important because when the ball gets close enough to the camera, we actually can’t track the ball and have to assume our dynamic models describe its motion accurately enough until it bounces back up.
What keeps Cassie from juggling indefinitely?
There are a few factors that affect how long Cassie can sustain a juggle. While in simulation the paddle exhibits homogeneous properties like its stiffness and damping, in reality every surface has anisotropic contact properties. So, there are parts of the paddle which may be better for juggling than others (and importantly, react differently than modeled). These differences in contact are also exacerbated due to how the paddle is cantilevered when mounted on Cassie. When the ball hits these areas, it leads to a larger than expected error in a juggle. Due to the small size of the paddle, the ball may then just hit the paddle’s edge and end the juggling run. Over a very long run, this is a likely occurrence. Additionally, some large juggling errors could cause Cassie’s feet to slip slightly, which ends up changing the stable standing position over time. Since this version of the controller assumes Cassie is stationary, this change in position eventually leads to poor juggles and failure.
Would Cassie be able to juggle while walking (or hovershoe-ing)?
Walking (and hovershoe-ing) while juggling is a far more challenging problem and is certainly a goal for future research. Some of these challenges include getting the paddle to precise poses to juggle the ball while also moving to avoid any destabilizing effects of stepping incorrectly. The number of juggles per step of walking could also vary and make the mathematics of the problem more challenging. The controller goal is also more involved. While the current goal of the juggling controller is to juggle the ball to a static apex position, with a walking juggling controller, we may instead want to hit the ball forwards and also walk forwards to bounce it, juggle the ball along a particular path, etc. Solving such challenges would be the main thrusts of the follow-up research.
Can you give an example of a practical task that would be made possible by using a controller like this?
Studying juggling means studying contact behavior and leveraging our models of it to achieve a known objective. Juggling could also be used to study predictable post-contact flight behavior. Consider the scenario where a robot is attempting to make a catch, but fails, letting the ball to bounce off of its hand, and then recovering the catch. This behavior could also be intentional: It is often easier to first execute a bounce to direct the target and then perform a subsequent action. For example, volleyball players could in principle directly hit a spiked ball back, but almost always bump the ball back up and then return it.
Even beyond this motivating example, the kinds of models we employ to get juggling working are more generally applicable to any task that involves contact, which could include tasks besides bouncing like sliding and rolling. For example, clearing space on a desk by pushing objects to the side may be preferable than individually manipulating each and every object on it.
You mention collaborative juggling or juggling multiple balls—is that something you’ve tried yet? Can you talk a bit more about what you’re working on next?
We haven’t yet started working on collaborative or multi-ball juggling, but that’s also a goal for future work. Juggling multiple balls statically is probably the most reasonable next goal, but presents additional challenges. For instance, you have to encode a notion of juggling urgency (if the second ball isn’t hit hard enough, you have less time to get the first ball up before you get back to the second one).
On the other hand, collaborative human-robot juggling requires a more advanced decision-making framework. To get robust multi-agent juggling, the robot will need to employ some sort of probabilistic model of the expected human behavior (are they likely to move somewhere? Are they trying to catch the ball high or low? Is it safe to hit the ball back?). In general, developing such human models is difficult since humans are fairly unpredictable and often don’t exhibit rational behavior. This will be a focus of future work.
[ Hybrid Robotics Lab ] Continue reading
Posted in Human Robots
#436140 Let’s Build Robots That Are as Smart ...
Illustration: Nicholas Little
Let’s face it: Robots are dumb. At best they are idiot savants, capable of doing one thing really well. In general, even those robots require specialized environments in which to do their one thing really well. This is why autonomous cars or robots for home health care are so difficult to build. They’ll need to react to an uncountable number of situations, and they’ll need a generalized understanding of the world in order to navigate them all.
Babies as young as two months already understand that an unsupported object will fall, while five-month-old babies know materials like sand and water will pour from a container rather than plop out as a single chunk. Robots lack these understandings, which hinders them as they try to navigate the world without a prescribed task and movement.
But we could see robots with a generalized understanding of the world (and the processing power required to wield it) thanks to the video-game industry. Researchers are bringing physics engines—the software that provides real-time physical interactions in complex video-game worlds—to robotics. The goal is to develop robots’ understanding in order to learn about the world in the same way babies do.
Giving robots a baby’s sense of physics helps them navigate the real world and can even save on computing power, according to Lochlainn Wilson, the CEO of SE4, a Japanese company building robots that could operate on Mars. SE4 plans to avoid the problems of latency caused by distance from Earth to Mars by building robots that can operate independently for a few hours before receiving more instructions from Earth.
Wilson says that his company uses simple physics engines such as PhysX to help build more-independent robots. He adds that if you can tie a physics engine to a coprocessor on the robot, the real-time basic physics intuitions won’t take compute cycles away from the robot’s primary processor, which will often be focused on a more complicated task.
Wilson’s firm occasionally still turns to a traditional graphics engine, such as Unity or the Unreal Engine, to handle the demands of a robot’s movement. In certain cases, however, such as a robot accounting for friction or understanding force, you really need a robust physics engine, Wilson says, not a graphics engine that simply simulates a virtual environment. For his projects, he often turns to the open-source Bullet Physics engine built by Erwin Coumans, who is now an employee at Google.
Bullet is a popular physics-engine option, but it isn’t the only one out there. Nvidia Corp., for example, has realized that its gaming and physics engines are well-placed to handle the computing demands required by robots. In a lab in Seattle, Nvidia is working with teams from the University of Washington to build kitchen robots, fully articulated robot hands and more, all equipped with Nvidia’s tech.
When I visited the lab, I watched a robot arm move boxes of food from counters to cabinets. That’s fairly straightforward, but that same robot arm could avoid my body if I got in its way, and it could adapt if I moved a box of food or dropped it onto the floor.
The robot could also understand that less pressure is needed to grasp something like a cardboard box of Cheez-It crackers versus something more durable like an aluminum can of tomato soup.
Nvidia’s silicon has already helped advance the fields of artificial intelligence and computer vision by making it possible to process multiple decisions in parallel. It’s possible that the company’s new focus on virtual worlds will help advance the field of robotics and teach robots to think like babies.
This article appears in the November 2019 print issue as “Robots as Smart as Babies.” Continue reading
Posted in Human Robots