オンラインアドミタンス残差学習による接触の多いマニピュレーションスキルの効率的なSim-to-real転移
Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning
シミュレーションで強化学習により動作とコンプライアンス制御パラメータを獲得し、実機では力センサを用いて制御パラメータの残差をオンライン学習することで、接触の多い組立・回転・ねじ締めタスクを効率的に実世界へ転移する手法を提案。
著者: Xiang Zhang, Changhao Wang, Lingfeng Sun, Zheng Wu, Xinghao Zhu, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
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.
