Proprioceptive Robot Collision Detection through Gaussian Process Regression
Robotics
2019-11-13 v1
Abstract
This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.
Cite
@article{arxiv.1905.08689,
title = {Proprioceptive Robot Collision Detection through Gaussian Process Regression},
author = {Dalla Libera Alberto and Tosello Elisa and Pillonetto Gianluigi and Ghidoni Stefano and Carli Ruggero},
journal= {arXiv preprint arXiv:1905.08689},
year = {2019}
}
Comments
Published at ACC 2019