A Robot Skill Learning Method Based on Improved Stable Estimator of Dynamical Systems

被引：0
作者：
Jin C.-C. [1 ]
Liu A.-D. [1 ]
Liu S. [2 ]
Zhang W.-A. [1 ]
机构：
[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou
[2] Department of Electrical and Computer Engineering, University of Kaiserslautern, Kaiserslautern
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Bayesian nonparametric model; dynamical system; Gaussian mixture model; Learning from demonstration; Lyapunov function;
D O I：
10.16383/j.aas.c200341
中图分类号：
学科分类号：
摘要：
This paper presents a novel robot skill learning method based on improved stable estimator of dynamical systems (SEDS). The original SEDS method can ensure the global stability of the learning system through nonlinear optimization. However, it cannot automatically determine the optimal number of Gaussian components and is difficult to make a trade-off between reconcile the stability and accuracy. Therefore, note that the Bayesian nonparametric model can be used to determine the appropriate number of components, the Dirichlet process Gaussian mixture model is applied to perform the initial fitting of the demonstrations in this paper. Then, the stability constraints are reformulated by using the parameterized Lyapunov function. The problems of stability and accuracy in the SEDS method are solved effectively. Finally, experiments on a LASA dataset and a Franka-panda cooperative robot validate the effectiveness and superiority of the proposed method. © 2022 Science Press. All rights reserved.
引用
收藏
页码：1771 / 1781
页数：10
相关论文
共 28 条
  • [1] Zeng Chao, Yang Chen-Guang, Li Qiang, Dai Shi-Lu, Research progress on human-robot skill transfer, Acta Automatica Sinica, 45, 10, pp. 1813-1828, (2019)
  • [2] Liu Nai-Jun, Lu Tao, Cai Ying-Hao, Wang Shuo, A review of robot manipulation skills learning methods, Acta Automatica Sinica, 45, 3, pp. 458-470, (2019)
  • [3] Qin Fang-Bo, Xu De, Review of robot manipulation skill models, Acta Automatica Sinica, 45, 8, pp. 1401-1418, (2019)
  • [4] Ravichandar H, Polydoros A S, Chernova S, Billard A., Recent advances in robot learning from demonstration, Annual Review of Control, Robotics, and Autonomous Systems, 3, 1, (2020)
  • [5] Brock O, Khatib O., Elastic Strips: A Framework for Integrated Planning and Execution, pp. 329-338, (2000)
  • [6] Billard A, Hayes G., DRAMA, A connectionist architecture for control and learning in autonomous robots, Adaptive Behavior, 7, 1, (1999)
  • [7] Ijspeert A J, Nakanishi J, Hoffmann H, Pastor P, Stefan Schaal, Dynamical movement primitives: Learning attractor models for motor behaviors, Neural Computation, 25, 2, (2012)
  • [8] Gribovskaya E, Khansari-Zadeh S M, Billard A., Learning nonlinear multi-variate dynamics of motion in robotic manipulators, International Journal of Robotics Research, 30, 1, pp. 80-117, (2011)
  • [9] Khansari-Zadeh S M, Billard A., Learning stable nonlinear dynamical systems with Gaussian mixture models, IEEE Transactions on Robotics, 27, 5, pp. 943-957, (2011)
  • [10] Yang C G, Chen C Z, He W, Cui R X, Li Z J., Robot learning system based on adaptive neural control and dynamic movement primitives, IEEE Transactions on Neural Networks and Learning Systems, 30, 3, (2019)
123