Kavli Affiliate: Xiang Zhang
| First 5 Authors: Xiang Zhang, Changhao Wang, Lingfeng Sun, Zheng Wu, Xinghao Zhu
| Summary:
Learning contact-rich manipulation skills is essential. Such skills require
the robots to interact with the environment with feasible manipulation
trajectories and suitable compliance control parameters to enable safe and
stable contact. However, learning these skills is challenging due to data
inefficiency in the real world and the sim-to-real gap in simulation. In this
paper, we introduce a hybrid offline-online framework to learn robust
manipulation skills. We employ model-free reinforcement learning for the
offline phase to obtain the robot motion and compliance control parameters in
simulation RV{with domain randomization}. Subsequently, in the online phase,
we learn the residual of the compliance control parameters to maximize robot
performance-related criteria with force sensor measurements in real time. To
demonstrate the effectiveness and robustness of our approach, we provide
comparative results against existing methods for assembly, pivoting, and
screwing tasks.
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