Improved Learning Accuracy for Learning Stable Control from Human Demonstrations*

Learning from Demonstration (LfD) has been identified as an effective method for making robots adapt to a similar kind of tasks. In this work, a framework of learning from demonstration has been proposed for modelling robot motions. We present an approach based on dimension ascending to learn a dynamical system, so that the reproduced motions can closely follow the demonstrations. In addition, the reproductions can ultimately reach and stop at the target, which reflects the robustness of the method. Therefore, the system accuracy and stability can be better guaranteed simultaneously. The effectiveness of the proposed approach is verified by performing handwriting experiments on the LASA data set.

[1]  Klaus Neumann,et al.  Neurally imprinted stable vector fields , 2013, ESANN.

[2]  Ning Sun,et al.  Minimum-Time Trajectory Planning for Underactuated Overhead Crane Systems With State and Control Constraints , 2014, IEEE Transactions on Industrial Electronics.

[3]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[4]  Changyin Sun,et al.  Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[6]  Chao Xu,et al.  Fast and Stable Learning of Dynamical Systems Based on Extreme Learning Machine , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[8]  Aude Billard,et al.  Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions , 2014, Robotics Auton. Syst..

[9]  Aude Billard,et al.  Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models , 2011, IEEE Transactions on Robotics.

[10]  Klaus Neumann,et al.  Learning robot motions with stable dynamical systems under diffeomorphic transformations , 2015, Robotics Auton. Syst..

[11]  Aude Billard,et al.  Incremental motion learning with locally modulated dynamical systems , 2015, Robotics Auton. Syst..

[12]  Guoqiang Hu,et al.  Adaptive Task-Space Cooperative Tracking Control of Networked Robotic Manipulators Without Task-Space Velocity Measurements , 2016, IEEE Transactions on Cybernetics.

[13]  Jun Nakanishi,et al.  Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors , 2013, Neural Computation.

[14]  Tao Liu,et al.  Eye-in-Hand Tracking Control of a Free-Floating Space Manipulator , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Aude Billard,et al.  Learning from Humans , 2016, Springer Handbook of Robotics, 2nd Ed..

[16]  Klaus Neumann,et al.  Neural learning of stable dynamical systems based on data-driven Lyapunov candidates , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.