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Workshop: Deep Reinforcement Learning

From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation

Yuzhe Qin · Hao Su · Xiaolong Wang


We introduce a novel single-camera teleoperation system for learning dexterous manipulation. Our system allows human operators to collect 3D demonstrations efficiently with only an iPad and a computer. These demonstrations are then used for imitation learning on complex multi-finger robot hand manipulation tasks. One key contribution of our system is that we construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand. This not only avoids unstable human-robot hand retargetting during data collection, but also provides a more intuitive and personalized interface for different users to operate on. Once the data collection is done, the customized robot hand trajectories can be converted to different specified robot hands (models that are manufactured and commercialized) to generate training demonstrations. Using the data collected on the customized hand, our imitation learning results show large improvement over pure RL on multiple specified robot hands.

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