TY - JOUR N2 - Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator. L1 - http://journals.pan.pl/Content/111781/PDF/06_201-212_00929_Bpast.No.67-2_06.02.20.pdf L2 - http://journals.pan.pl/Content/111781 PY - 2019 IS - No. 2 EP - 212 DO - 10.24425/bpas.2019.128116 KW - variable impedance control KW - reinforcement learning KW - efficient KW - Gaussian process KW - industrial robots A1 - Li, C. A1 - Zhang, Z. A1 - Xia, G. A1 - Xie, X. A1 - Zhu, Q. VL - 67 DA - 30.04.2019 T1 - Efficient learning variable impedance control for industrial robots SP - 201 UR - http://journals.pan.pl/dlibra/publication/edition/111781 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -