Using Backpropagation to Learn the Dynamics of a Real Robot Arm

Abstract : Computing the inverse dynamics of a robot arm is an active area of research in the control literature. We apply a back propagation network to this problem and measure its performance on the CMU Direct-Drive Arm II for a family of pick and place trajectories. Trained on a random sample of these trajectories, the network is shown to generalize top new samples drawn from the same family. The weights developed during the learning phase are reminiscent of the velocity and acceleration filters used in standard control theory. Keywords: Robotics; Manipulators; Neural networks; Robot control; Learning manipulator dynamics; Artificial intelligence.

[1]  A. Bejczy.  Robot arm dynamics and control , 1974 .

[2]  H. Hemami,et al.  Nonlinear Adaptive Control of an N-Link Robot with Unknown Load , 1987 .

[3]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[4]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

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

[6]  Carlos Canudas de Wit,et al.  Adaptive friction compensation in DC motor drives , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[7]  John M. Hollerbach,et al.  A Recursive Lagrangian Formulation of Maniputator Dynamics and a Comparative Study of Dynamics Formulation Complexity , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Alex Waibel,et al.  Phoneme Recognition: Neural Networks vs , 1988 .

[9]  Jin‐Xi Zhang,et al.  Adaptive weighted suboptimal control for linear dynamic systems having a polynomial input , 1987 .

[10]  Matthew T. Mason,et al.  Robot Motion: Planning and Control , 1983 .

[11]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[12]  M. H. Raibert,et al.  Manipulator control using the configuration space method , 1978 .

[13]  J. Slotine,et al.  On the Adaptive Control of Robot Manipulators , 1987 .

[14]  W. Thomas Miller,et al.  Sensor-based control of robotic manipulators using a general learning algorithm , 1987, IEEE J. Robotics Autom..

[15]  Mark L. Nagurka,et al.  A Fourier-Based Optimal Control Approach for Structural Systems , 1988, 1988 American Control Conference.

[16]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[17]  Alex Waibel,et al.  Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.