Learning Bipedal Robot Locomotion from Human Movement
This work addresses the challenge of imparting humanlike movement to robots for applications in robotics and human-robot interaction, representing an incremental advance in simulation-to-reality transfer.
The authors tackled the problem of teaching a bipedal robot humanlike locomotion from motion capture data, achieving a method that transitions from simulation to physical execution without real-world training and preserves movement style with safe failure modes.
Teaching an anthropomorphic robot from human example offers the opportunity to impart humanlike qualities on its movement. In this work we present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from human motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot without requiring any real world training iterations or offline steps. To overcome the disparity in joint configurations between the robot and the motion capture actor, our method incorporates motion re-targeting into the training process. Domain randomization techniques are used to compensate for the differences between the simulated and physical systems. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving. Our controller preserves the style imparted by the motion capture data and exhibits graceful failure modes resulting in safe operation for the robot. This work was performed for research purposes only.