@ARTICLE{Li_C._Efficient_2019,
 author={Li, C. and Zhang, Z. and Xia, G. and Xie, X. and Zhu, Q.},
 volume={67},
 number={No. 2},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={201-212},
 howpublished={online},
 year={2019},
 abstract={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.},
 type={Artykuły / Articles},
 title={Efficient learning variable impedance control for industrial robots},
 URL={http://www.journals.pan.pl/Content/111781/PDF/06_201-212_00929_Bpast.No.67-2_06.02.20.pdf},
 doi={10.24425/bpas.2019.128116},
 keywords={variable impedance control, reinforcement learning, efficient, Gaussian process, industrial robots},
}