Learning inverse dynamics for redundant manipulator control

High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system. This can be problematic in the presence of modeling uncertainties as the performance of classical, modelbased controllers is highly dependant upon accurate knowledge of the system. In addition, future robotic systems such as humanoids are likely to be redundant, requiring a mechanism for redundancy resolution when performing lower degree-of-freedom tasks. In this paper, a learning approach to estimating the inverse dynamic equations is presented. Locally Weighted Projection Regression (LWPR) is used to learn the inverse dynamics of a manipulator in both joint and task space and the resulting controllers are used to drive a 3 and 4 DOF robot in simulation. The performance of the learning controllers is compared to a traditional model based control method and is also shown to be a viable control method for a redundant system.

[1]  A. Liegeois,et al.  Automatic supervisory control of the configuration and behavior of multi-body mechanisms , 1977 .

[2]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[3]  Oussama Khatib,et al.  A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..

[4]  S. Shankar Sastry,et al.  Adaptive Control of Mechanical Manipulators , 1987, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[5]  Bruno Siciliano,et al.  Resolved-acceleration control of robot manipulators: A critical review with experiments , 1998, Robotica.

[6]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[7]  Jun Nakanishi,et al.  Operational Space Control: A Theoretical and Empirical Comparison , 2008, Int. J. Robotics Res..

[8]  Stefan Schaal,et al.  Learning inverse kinematics , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[9]  Christopher G. Atkeson,et al.  Using locally weighted regression for robot learning , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[10]  Bruno Siciliano,et al.  Modelling and Control of Robot Manipulators , 1997, Advanced Textbooks in Control and Signal Processing.

[11]  Peter I. Corke,et al.  A robotics toolbox for MATLAB , 1996, IEEE Robotics Autom. Mag..

[12]  Stefan Schaal,et al.  Learning to Control in Operational Space , 2008, Int. J. Robotics Res..

[13]  Gentiane Venture,et al.  Identification of humanoid robots dynamics using floating-base motion dynamics , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Christopher G. Atkeson,et al.  Constructive Incremental Learning from Only Local Information , 1998, Neural Computation.

[15]  Bernhard Schölkopf,et al.  Learning Inverse Dynamics: a Comparison , 2008, ESANN.

[16]  Tamim Asfour,et al.  Human-like motion of a humanoid robot arm based on a closed-form solution of the inverse kinematics problem , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[17]  Stefan Schaal,et al.  Inverse kinematics for humanoid robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[18]  Stefan Schaal,et al.  Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning , 2002, Applied Intelligence.

[19]  Bernhard Schölkopf,et al.  Sparse online model learning for robot control with support vector regression , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  John M. Hollerbach,et al.  Redundancy resolution of manipulators through torque optimization , 1987, IEEE J. Robotics Autom..