Neural internal model control adds a rigid-body predictive error, the gap between commanded and actual body motion, as a feedback signal to an RL policy, improving disturbance robustness on quadrotors and quadrupeds.
Learning Agile Bipedal Motions on a Quadrupedal Robot
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abstract
Can a quadrupedal robot perform bipedal motions like humans? Although developing human-like behaviors is more often studied on costly bipedal robot platforms, we present a solution over a lightweight quadrupedal robot that unlocks the agility of the quadruped in an upright standing pose and is capable of a variety of human-like motions. Our framework is with a hierarchical structure. At the low level is a motion-conditioned control policy that allows the quadrupedal robot to track desired base and front limb movements while balancing on two hind feet. The policy is commanded by a high-level motion generator that gives trajectories of parameterized human-like motions to the robot from multiple modalities of human input. We for the first time demonstrate various bipedal motions on a quadrupedal robot, and showcase interesting human-robot interaction modes including mimicking human videos, following natural language instructions, and physical interaction. The video is available at https://sites.google.com/view/bipedal-motions-quadruped.
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Neural Internal Model Control: Learning a Robust Control Policy via Predictive Error Feedback
Neural internal model control adds a rigid-body predictive error, the gap between commanded and actual body motion, as a feedback signal to an RL policy, improving disturbance robustness on quadrotors and quadrupeds.