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Fast Robot Motor Skill Acquisition Based on Bayesian Inspired Policy Improvement

  • Jian FuEmail author
  • Siyuan Shen
  • Ce Cao
  • Cong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11745)

Abstract

Learning from demonstration with the reinforcement learning (LfDRL) framework has been successfully applied to acquire the skill of robot movement. However, the optimization process of LfDRL usually converges slowly on the condition that new task is considerable different from imitation task. We in this paper proposes a ProMPs-Bayesian-PI\(^2\) algorithms to expedite the transfer process. The main ideas is adding new heuristic information to guide optimization search other than random search from the stats of imitation learning. Specifically, we use the result of Bayesian estimation as the heuristic information to guide the PI\(^2\) when it random search. Finally, we verify this method by UR5 and compare it with the traditional method of ProMPs-PI\(^2\). The experimental results show that this method is feasible and effective.

Keywords

Motion planning Path integral Bayesian estimation Probabilistic movement primitives 

References

  1. Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.: Interaction primitives for human-robot cooperation tasks. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2831–2837. IEEE (2014).  https://doi.org/10.1109/ICRA.2014.6907265
  2. Yang, C., Chen, C., He, W., Cui, R., Li, Z.: Robot learning system based on adaptive neural control and dynamic movement primitives. IEEE Trans. Neural Netw. Learn. Syst. 30, 777–787 (2018)MathSciNetCrossRefGoogle Scholar
  3. Deisenroth, M.P., Fox, D., Rasmussen, C.E.: Gaussian processes for data-efficient learning in robotics and control. IEEE Trans. Pattern Anal. Mach. Intell. 37(2), 408–423 (2015)CrossRefGoogle Scholar
  4. Fu, J., Ning, L., Wei, S., Zhang, L.: A novel DS-GMR coupled primitive for robotic motion skill learning. In: 2015 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration, Wuhan, China, pp. 111–115 (2015a)Google Scholar
  5. Fu, J., Wei, S., Ning, L., Xiang, K.: GMR based forcing term learning for DMPs. In: 2015 Chinese Automation Congress, Wuhan, China, pp. 437–442 (2015b)Google Scholar
  6. Havoutis, I., Calinon, S.: Learning from demonstration for semi-autonomous teleoperation. Auton. Robots 43, 1–14 (2018)Google Scholar
  7. Khoramshahi, M., Billard, A.: A dynamical system approach to task-adaptation in physical human-robot interaction. Auton. Robots 43(4), 927–946 (2019)CrossRefGoogle Scholar
  8. Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.: A Kernel-based approach to learning contact distributions for robot manipulation tasks. Auton. Robots 42(3), 581–600 (2018)CrossRefGoogle Scholar
  9. Mirrazavi Salehian, S.S., Figueroa Fernandez, N.B., Billard, A.: Dynamical system-based motion planning for multi-arm systems: reaching for moving objects (2017)Google Scholar
  10. Paraschos, A., Rueckert, E., Peters, J., Neumann, G.: Probabilistic movement primitives under unknown system dynamics. Adv. Robot.: Int. J. Robot. Soc. Jpn. 32(5–6), 297–310 (2018)CrossRefGoogle Scholar
  11. Pervez, A., Lee, D.: Learning task-parameterized dynamic movement primitives using mixture of GMMS. Intell. Serv. Robot. 11(1), 61–78 (2018)CrossRefGoogle Scholar
  12. Schaarschmidt, M., Kuhnle, A., Ellis, B., Fricke, K., Gessert, F., Yoneki, E.: Lift: reinforcement learning in computer systems by learning from demonstrations. Mach. Learn. (2018)Google Scholar
  13. Sigaud, O., Salaun, C., Padois, V.: On-line regression algorithms for learning mechanical models of robots: a survey. Robot. Auton. Syst. 59(12), 1115–1129 (2011)CrossRefGoogle Scholar
  14. Theodorou, E., Buchli, J., Schaal, S.: A generalized path integral control approach to reinforcement learning. J. Mach. Learn. Res. 11, 3137–3181 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of AutomationWuhan University of TechnologyWuhanChina

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