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Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
Cited 3 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2022-06
- Publisher
- Robotics: Science and Systems
- Citation
- Robotics: Science and Systems
- Abstract
- A motion-based control interface promises flexible robot operations in dangerous environments by combining user intuitions with the robot's motor capabilities. However, designing a motion interface for non-humanoid robots, such as quadrupeds or hexapods, is not straightforward because different dynamics and control strategies govern their movements. We propose a novel motion control system that allows a human user to operate various motor tasks seamlessly on a quadrupedal robot. We first retarget the captured human motion into the corresponding robot motion with proper semantics using supervised learning and post-processing techniques. Then we apply the motion imitation learning with curriculum learning to develop a control policy that can track the given retargeted reference. We further improve the performance of both motion retargeting and motion imitation by training a set of experts. As we demonstrate, a user can execute various motor tasks using our system, including standing, sitting, tilting, manipulating, walking, and turning, on simulated and real quadrupeds. We also conduct a set of studies to analyze the performance gain induced by each component.(Video(1))
- ISSN
- 2330-7668
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