Tag Archives: mit
#439559 MIT is Building a Dynamic, Acrobatic ...
For a long time, having a bipedal robot that could walk on a flat surface without falling over (and that could also maybe occasionally climb stairs or something) was a really big deal. But we’re more or less past that now. Thanks to the talented folks at companies like Agility Robotics and Boston Dynamics, we now expect bipedal robots to meet or exceed actual human performance for at least a small subset of dynamic tasks. The next step seems to be to find ways of pushing the limits of human performance, which it turns out means acrobatics. We know that IHMC has been developing their own child-size acrobatic humanoid named Nadia, and now it sounds like researchers from Sangbae Kim’s lab at MIT are working on a new acrobatic robot of their own.
We’ve seen a variety of legged robots from MIT’s Biomimetic Robotics Lab, including Cheetah and HERMES. Recently, they’ve been doing a bunch of work with their spunky little Mini Cheetahs (developed with funding and support from Naver Labs), which are designed for some dynamic stuff like gait exploration and some low-key four-legged acrobatics.
In a paper recently posted to arXiv (to be presented at Humanoids 2020 in July), Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim describe “a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the humanoid robot.” So it’s not just the robot itself, but all of the software infrastructure necessary to get it to do what they want it to do.
MIT Humanoid performing a back flip off of a humanoid robot off of a 0.4 m platform in simulation.
Image: MIT
First let’s talk about the hardware that we’ll be looking at once the MIT Humanoid makes it out of simulation. It’s got the appearance of a sort of upright version of Mini Cheetah, but that appearance is deceiving, says MIT’s Matt Chignoli. While the robot’s torso and arms are very similar to Mini Cheetah, the leg design is totally new and features redesigned actuators with higher power and better torque density. “The main focus of the leg design is to enable smooth but dynamic ‘heel-to-toe’ actions that happen in humans’ walking and running, while maintaining low inertia for smooth interactions with ground contacts,” Chignoli told us in an email. “Dynamic ankle actions have been rare in humanoid robots. We hope to develop robust, low inertia and powerful legs that can mimic human leg actions.”
The design strategy matters because the field of humanoid robots is presently dominated by hydraulically actuated robots and robots with series elastic actuators. As we continue to improve the performance of our proprioceptive actuator technology, as we have done for this work, we aim to demonstrate that our unique combination of high torque density, high bandwidth force control, and the ability to mitigate impacts is optimal for highly dynamic locomotion of any legged robot, including humanoids.
-Matt Chignoli
Now, it’s easy to say “oh well pfft that’s just in simulation and you can get anything to work in simulation,” which, yeah, that’s kinda true. But MIT is putting a lot of work into accurately simulating everything that they possibly can—in particular, they’re modeling the detailed physical constraints that the robot operates under as it performs dynamic motions, allowing the planner to take those constraints into account and (hopefully) resulting in motions that match the simulation pretty accurately.
“When it comes to the physical capabilities of the robot, anything we demonstrate in simulation should be feasible on the robot,” Chignoli says. “We include in our simulations detailed models for the robot’s actuators and battery, models that have been validated experimentally. Such detailed models are not frequently included in dynamic simulations for robots.” But simulation is still simulation, of course, and no matter how good your modeling is, that transfer can be tricky, especially when doing highly dynamic motions.
“Despite our confidence in our simulator’s ability to accurately mimic the physical capabilities of our robot with high fidelity, there are aspects of our simulator that remain uncertain as we aim to deploy our acrobatic motions onto hardware,” Chignoli explains. “The main difficulty we see is state estimation. We have been drawing upon research related to state estimation for drones, which makes use of visual odometry. Without having an assembled robot to test these new estimation strategies on, though, it is difficult to judge the simulation to real transfer for these types of things.”
We’re told that the design of the MIT Humanoid is complete, and that the plan is to build it for real over the summer, with the eventual goal of doing parkour over challenging terrains. It’s tempting to fixate on the whole acrobatics and parkour angle of things (and we’re totally looking forward to some awesome videos), but according to Chignoli, the really important contribution here is the framework rather than the robot itself:
The acrobatic motions that we demonstrate on our small-scale humanoid are less about the actual acrobatics and more about what the ability to perform such feats implies for both our hardware as well as our control framework. The motions are important in terms of the robot’s capabilities because we are proving, at least in simulation, that we can replicate the dynamic feats of Boston Dynamics’ ATLAS robot using an entirely different actuation scheme (proprioceptive electromagnetic motors vs. hydraulic actuators, respectively). Verification that proprioceptive actuators can achieve the necessary torque density to perform such motions while retaining the advantages of low mechanical impedance and high-bandwidth torque control is important as people consider how to design the next generation of dynamic humanoid robots. Furthermore, the acrobatic motions demonstrate the ability of our “actuator-aware” motion planner to generate feasible motion plans that push the boundaries of what our robot can do.
The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors, by Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim from MIT and UMass Amherst, will be presented at Humanoids 2020 this July. You can read a preprint on arXiv here. Continue reading
Posted in Human Robots
#439286 MIT is Building a Dynamic, Acrobatic ...
For a long time, having a bipedal robot that could walk on a flat surface without falling over (and that could also maybe occasionally climb stairs or something) was a really big deal. But we’re more or less past that now. Thanks to the talented folks at companies like Agility Robotics and Boston Dynamics, we now expect bipedal robots to meet or exceed actual human performance for at least a small subset of dynamic tasks. The next step seems to be to find ways of pushing the limits of human performance, which it turns out means acrobatics. We know that IHMC has been developing their own child-size acrobatic humanoid named Nadia, and now it sounds like researchers from Sangbae Kim’s lab at MIT are working on a new acrobatic robot of their own.
We’ve seen a variety of legged robots from MIT’s Biomimetic Robotics Lab, including Cheetah and HERMES. Recently, they’ve been doing a bunch of work with their spunky little Mini Cheetahs (developed with funding and support from Naver Labs), which are designed for some dynamic stuff like gait exploration and some low-key four-legged acrobatics.
In a paper recently posted to arXiv (to be presented at Humanoids 2020 in July), Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim describe “a new humanoid robot design, an actuator-aware kino-dynamic motion planner, and a landing controller as part of a practical system design for highly dynamic motion control of the humanoid robot.” So it’s not just the robot itself, but all of the software infrastructure necessary to get it to do what they want it to do.
Image: MIT
MIT Humanoid performing a back flip off of a humanoid robot off of a 0.4 m platform in simulation.
First let’s talk about the hardware that we’ll be looking at once the MIT Humanoid makes it out of simulation. It’s got the appearance of a sort of upright version of Mini Cheetah, but that appearance is deceiving, says MIT’s Matt Chignoli. While the robot’s torso and arms are very similar to Mini Cheetah, the leg design is totally new and features redesigned actuators with higher power and better torque density. “The main focus of the leg design is to enable smooth but dynamic ‘heel-to-toe’ actions that happen in humans’ walking and running, while maintaining low inertia for smooth interactions with ground contacts,” Chignoli told us in an email. “Dynamic ankle actions have been rare in humanoid robots. We hope to develop robust, low inertia and powerful legs that can mimic human leg actions.”
The design strategy matters because the field of humanoid robots is presently dominated by hydraulically actuated robots and robots with series elastic actuators. As we continue to improve the performance of our proprioceptive actuator technology, as we have done for this work, we aim to demonstrate that our unique combination of high torque density, high bandwidth force control, and the ability to mitigate impacts is optimal for highly dynamic locomotion of any legged robot, including humanoids.
-Matt Chignoli
Now, it’s easy to say “oh well pfft that’s just in simulation and you can get anything to work in simulation,” which, yeah, that’s kinda true. But MIT is putting a lot of work into accurately simulating everything that they possibly can—in particular, they’re modeling the detailed physical constraints that the robot operates under as it performs dynamic motions, allowing the planner to take those constraints into account and (hopefully) resulting in motions that match the simulation pretty accurately.
“When it comes to the physical capabilities of the robot, anything we demonstrate in simulation should be feasible on the robot,” Chignoli says. “We include in our simulations detailed models for the robot’s actuators and battery, models that have been validated experimentally. Such detailed models are not frequently included in dynamic simulations for robots.” But simulation is still simulation, of course, and no matter how good your modeling is, that transfer can be tricky, especially when doing highly dynamic motions.
“Despite our confidence in our simulator’s ability to accurately mimic the physical capabilities of our robot with high fidelity, there are aspects of our simulator that remain uncertain as we aim to deploy our acrobatic motions onto hardware,” Chignoli explains. “The main difficulty we see is state estimation. We have been drawing upon research related to state estimation for drones, which makes use of visual odometry. Without having an assembled robot to test these new estimation strategies on, though, it is difficult to judge the simulation to real transfer for these types of things.”
We’re told that the design of the MIT Humanoid is complete, and that the plan is to build it for real over the summer, with the eventual goal of doing parkour over challenging terrains. It’s tempting to fixate on the whole acrobatics and parkour angle of things (and we’re totally looking forward to some awesome videos), but according to Chignoli, the really important contribution here is the framework rather than the robot itself:
The acrobatic motions that we demonstrate on our small-scale humanoid are less about the actual acrobatics and more about what the ability to perform such feats implies for both our hardware as well as our control framework. The motions are important in terms of the robot’s capabilities because we are proving, at least in simulation, that we can replicate the dynamic feats of Boston Dynamics’ ATLAS robot using an entirely different actuation scheme (proprioceptive electromagnetic motors vs. hydraulic actuators, respectively). Verification that proprioceptive actuators can achieve the necessary torque density to perform such motions while retaining the advantages of low mechanical impedance and high-bandwidth torque control is important as people consider how to design the next generation of dynamic humanoid robots. Furthermore, the acrobatic motions demonstrate the ability of our “actuator-aware” motion planner to generate feasible motion plans that push the boundaries of what our robot can do.
The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors, by Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim from MIT and UMass Amherst, will be presented at Humanoids 2020 this July. You can read a preprint on arXiv here. Continue reading
Posted in Human Robots
#439241 The MIT humanoid robot: A dynamic ...
Creating robots that can perform acrobatic movements such as flips or spinning jumps can be highly challenging. Typically, in fact, these robots require sophisticated hardware designs, motion planners and control algorithms. 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