  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.
