Humanoid robot waltzes with the help of AI trained on human motion capture recordings
Xuxin Cheng and Mazeyu Ji
AI that helps humanoid robots mirror human movements could allow robots to walk, dance, and fight in more human-like ways.
The most agile and fluid robot movements, such as Boston Dynamics’ impressive demonstration of robotic acrobatics, are typically narrow, pre-programmed sequences. Teaching robots a wide repertoire of persuasive human movements remains difficult.
To overcome this hurdle, Xuanbin Peng and colleagues at the University of California, San Diego, developed an artificial intelligence system called ExBody2. This allows the robot to imitate various human movements in a more realistic way and execute them smoothly.
Peng and his team began by building a database of possible movements that a humanoid robot could perform, from simple movements such as standing and walking to more complex movements such as tricky dance moves. Created. The database contained motion capture recordings of hundreds of human volunteers collected in previous research projects.
“Humanoid robots share a similar physical structure with us, so it makes sense to leverage the vast amount of human movement data that is already available,” Peng says. “By learning to imitate this kind of behavior, robots can quickly learn a variety of human-like behaviors. This means that anything humans can do, robots have the potential to learn.” It means something.”
To teach the pseudo-humanoid robot how to move, Peng and his team used reinforcement learning. In this learning, the AI is given an example of what makes a successful move and then challenged to figure out how to do it yourself through trial and error. They started by training ExBody2 with full access to all the data on this virtual robot, including the coordinates of each joint, so it could mimic human movements as closely as possible. It then learned from these movements, using only data that was accessible in the real world, such as inertia and velocity measurements from sensors on the actual robot’s body.
After ExBody2 was trained on the database, it was able to control two different commercially available humanoid robots. It was able to smoothly combine simple movements such as walking in a straight line and crouching, as well as perform tricky movements such as following a 40-second dance routine, throwing punches, and waltzing with humans.
“Humanoid robots work best when all limbs and joints work together,” Penn says. “Many tasks and movements require coordination between the arms, legs, and torso, and whole-body coordination greatly increases the range of a robot’s capabilities.”
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