Tag Archives: legged

#439861 Researchers successfully build ...

As a robotics engineer, Yasemin Ozkan-Aydin, assistant professor of electrical engineering at the University of Notre Dame, gets her inspiration from biological systems. The collective behaviors of ants, honeybees and birds to solve problems and overcome obstacles is something researchers have developed in aerial and underwater robotics. Developing small-scale swarm robots with the capability to traverse complex terrain, however, comes with a unique set of challenges. Continue reading

Posted in Human Robots

#439495 Legged Robots Do Surprisingly Well in ...

Here on Earth, we’re getting good enough at legged robots that we’re starting to see a transition from wheels to legs for challenging environments, especially environments with some uncertainty as to exactly what kind of terrain your robot might encounter. Beyond Earth, we’re still heavily reliant on wheeled vehicles, but even that might be starting to change. While wheels do pretty well on the Moon and on Mars, there are lots of other places to explore, like smaller moons and asteroids. And there, it’s not just terrain that’s a challenge: it’s gravity.

In low gravity environments, any robot moving over rough terrain risks entering a flight phase. Perhaps an extended flight phase, depending on how low the gravity is, which can be dangerous to robots that aren’t prepared for it. Researchers at the Robotic Systems Lab at ETH Zurich have been doing some experiments with the SpaceBok quadruped, and they’ve published a paper in IEEE T-RO showing that it’s possible to teach SpaceBok to effectively bok around in low gravity environments while using its legs to reorient itself during flight, exhibiting “cat-like jumping and landing” behaviors through vigorous leg-wiggling.

Also, while I’m fairly certain that “bok” is not a verb that means “to move dynamically in low gravity using legs,” I feel like that’s what it should mean. Sort of like pronk, except in space. Let’s make it so!

Just look at that robot bok!

This reorientation technique was developed using deep reinforcement learning, and then transferred from simulation to a real SpaceBok robot, albeit in two degrees of freedom rather than three. The real challenge with this method is just how complicated things get when you start wiggling multiple limbs in the air trying to get to a specific configuration, since the dynamics here are (as the paper puts it) “highly non-linear,” and it proved somewhat difficult to even simulate everything well enough. What you see in the simulation, incidentally, is an environment similar to Ceres, the largest asteroid in the asteroid belt, which has a surface gravity of 0.03g.

Although SpaceBok has “space” right in the name, it’s not especially optimized for this particular kind of motion. As the video shows, having an actuated hip joint could make the difference between a reliable soft landing and, uh, not. Not landing softly is a big deal, because an uncontrolled bounce could send the robot flying huge distances, which is what happened to the Philae lander on comet 67P/Churyumov–Gerasimenko back in 2014.

For more details on SpaceBok’s space booking, we spoke with the paper’s first author, Nikita Rudin, via email.

IEEE Spectrum: Why are legs ideal for mobility in low gravity environments?

Rudin: In low gravity environments, rolling on wheels becomes more difficult because of reduced traction. However, legs can exploit the low gravity and use high jumps to move efficiently. With high jumps, you can also clear large obstacles along the way, which is harder to do in higher gravity.

Were there unique challenges to training your controller in 2D and 3D relative to training controllers for terrestrial legged robot motion?

The main challenge is the long flight phase, which is not present in terrestrial locomotion. In earth gravity, robots (and animals) use reaction forces from the ground to balance. During a jump, they don't usually need to re-orient themselves. In the case of low gravity, we have extended flight phases (multiple seconds) and only short contacts with the ground. The robot needs to be able to re-orient / balance in the air. Otherwise, a small disturbance at the moment of the jump will slowly flip the robot. In short, in low gravity, there is a new control problem that can be neglected on Earth.

Besides the addition of a hip joint, what other modifications would you like to make to the robot to enhance its capabilities? Would a tail be useful, for example? Or very heavy shoes?

A tail is a very interesting idea and heavy shoes would definitely help, however, they increase the total weight, which is costly in space. We actually add some minor weight to feet already (in the paper we analyze the effect of these weights). Another interesting addition would be a joint in the center of the robot allowing it to do cat-like backbone torsion.

How does the difficulty of this problem change as the gravity changes?

With changing gravity you change the importance of mid-air re-orientation compared to ground contacts. For locomotion, low-gravity is harder from the reasoning above. However, if the robot is dropped and needs to perform a flip before landing, higher gravity is harder because you have less time for the whole process.

What are you working on next?

We have a few ideas for the next projects including a legged robot specifically designed and certified for space and exploring cat-like re-orientation on earth with smaller/faster robots. We would also like to simulate a zero-g environment on earth by dropping the robot from a few dozens of meters into a safety net, and of course, a parabolic flight is still very much one of our objectives. However, we will probably need a smaller robot there as well.

Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning, by Nikita Rudin, Hendrik Kolvenbach, Vassilios Tsounis, and Marco Hutter from ETH Zurich, is published in IEEE Transactions on Robotics. Continue reading

Posted in Human Robots

#439372 Legged Robots Do Surprisingly Well in ...

Here on Earth, we’re getting good enough at legged robots that we’re starting to see a transition from wheels to legs for challenging environments, especially environments with some uncertainty as to exactly what kind of terrain your robot might encounter. Beyond Earth, we’re still heavily reliant on wheeled vehicles, but even that might be starting to change. While wheels do pretty well on the Moon and on Mars, there are lots of other places to explore, like smaller moons and asteroids. And there, it’s not just terrain that’s a challenge: it’s gravity.

In low gravity environments, any robot moving over rough terrain risks entering a flight phase. Perhaps an extended flight phase, depending on how low the gravity is, which can be dangerous to robots that aren’t prepared for it. Researchers at the Robotic Systems Lab at ETH Zurich have been doing some experiments with the SpaceBok quadruped, and they’ve published a paper in IEEE T-RO showing that it’s possible to teach SpaceBok to effectively bok around in low gravity environments while using its legs to reorient itself during flight, exhibiting “cat-like jumping and landing” behaviors through vigorous leg-wiggling.

Also, while I’m fairly certain that “bok” is not a verb that means “to move dynamically in low gravity using legs,” I feel like that’s what it should mean. Sort of like pronk, except in space. Let’s make it so!

Just look at that robot bok!

This reorientation technique was developed using deep reinforcement learning, and then transferred from simulation to a real SpaceBok robot, albeit in two degrees of freedom rather than three. The real challenge with this method is just how complicated things get when you start wiggling multiple limbs in the air trying to get to a specific configuration, since the dynamics here are (as the paper puts it) “highly non-linear,” and it proved somewhat difficult to even simulate everything well enough. What you see in the simulation, incidentally, is an environment similar to Ceres, the largest asteroid in the asteroid belt, which has a surface gravity of 0.03g.

Although SpaceBok has “space” right in the name, it’s not especially optimized for this particular kind of motion. As the video shows, having an actuated hip joint could make the difference between a reliable soft landing and, uh, not. Not landing softly is a big deal, because an uncontrolled bounce could send the robot flying huge distances, which is what happened to the Philae lander on comet 67P/Churyumov–Gerasimenko back in 2014.

For more details on SpaceBok’s space booking, we spoke with the paper’s first author, Nikita Rudin, via email.

IEEE Spectrum: Why are legs ideal for mobility in low gravity environments?

Rudin: In low gravity environments, rolling on wheels becomes more difficult because of reduced traction. However, legs can exploit the low gravity and use high jumps to move efficiently. With high jumps, you can also clear large obstacles along the way, which is harder to do in higher gravity.

Were there unique challenges to training your controller in 2D and 3D relative to training controllers for terrestrial legged robot motion?

The main challenge is the long flight phase, which is not present in terrestrial locomotion. In earth gravity, robots (and animals) use reaction forces from the ground to balance. During a jump, they don't usually need to re-orient themselves. In the case of low gravity, we have extended flight phases (multiple seconds) and only short contacts with the ground. The robot needs to be able to re-orient / balance in the air. Otherwise, a small disturbance at the moment of the jump will slowly flip the robot. In short, in low gravity, there is a new control problem that can be neglected on Earth.

Besides the addition of a hip joint, what other modifications would you like to make to the robot to enhance its capabilities? Would a tail be useful, for example? Or very heavy shoes?

A tail is a very interesting idea and heavy shoes would definitely help, however, they increase the total weight, which is costly in space. We actually add some minor weight to feet already (in the paper we analyze the effect of these weights). Another interesting addition would be a joint in the center of the robot allowing it to do cat-like backbone torsion.

How does the difficulty of this problem change as the gravity changes?

With changing gravity you change the importance of mid-air re-orientation compared to ground contacts. For locomotion, low-gravity is harder from the reasoning above. However, if the robot is dropped and needs to perform a flip before landing, higher gravity is harder because you have less time for the whole process.

What are you working on next?

We have a few ideas for the next projects including a legged robot specifically designed and certified for space and exploring cat-like re-orientation on earth with smaller/faster robots. We would also like to simulate a zero-g environment on earth by dropping the robot from a few dozens of meters into a safety net, and of course, a parabolic flight is still very much one of our objectives. However, we will probably need a smaller robot there as well.

Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning, by Nikita Rudin, Hendrik Kolvenbach, Vassilios Tsounis, and Marco Hutter from ETH Zurich, is published in IEEE Transactions on Robotics. Continue reading

Posted in Human Robots

#439174 A tactile sensing foot to increase the ...

In order to effectively navigate real-world environments, legged robots should be able to move swiftly and freely while maintaining their balance. This is particularly true for humanoid robots, robots with two legs and a human-like body structure. Continue reading

Posted in Human Robots

#439105 This Robot Taught Itself to Walk in a ...

Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.

And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.

It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.

This likely isn’t the first robot video you’ve seen, nor the most polished.

For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.

This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.

But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.

In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.

Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.

In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.

Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.

To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.

Once the algorithm was good enough, it graduated to Cassie.

And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.

Other labs have been hard at work applying machine learning to robotics.

Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.

And in the meantime, Boston Dynamics bots are testing the commercial waters.

Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”

The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.

Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading

Posted in Human Robots