ICRA 2011 Paper Abstract


Paper TuA207.3

Gijsberts, Arjan (Italian Institute of Technology), Metta, Giorgio (Istituto Italiano di Tecnologia (IIT))

Incremental Learning of Robot Dynamics using Random Features

Scheduled for presentation during the Regular Sessions "Biologically-Inspired Robots II" (TuA207), Tuesday, May 10, 2011, 10:35−10:50, Room 5B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Learning and Adaptive Systems, Direct/Inverse Dynamics Formulation


Analytical models for robot dynamics often perform suboptimally in practice, due to various non-linearities and the difficulty of accurately estimating the dynamic parameters. Machine learning techniques are less sensitive to these problems and therefore an interesting alternative for modeling robot dynamics. We propose a learning method that combines a least squares algorithm with a non-linear feature mapping and an efficient update rule. Using data from five different robots, we show that the method can accurately model manipulator dynamics, either when trained in batch or incrementally. Furthermore, the update time and memory usage of the method are bounded, therefore allowing use in real-time control loops.



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