Learning Force-Based Robot Skills from Haptic Demonstration

Locally weighted as well as Gaussian mixtures learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstration. Input streams other than visual information, as used in most applications up to date, reveal themselves as quite useful in trajectory learning experiments where visual sources are not available. For the first time, force/torque feedback through a haptic device has been used for teaching a teleoperated robot to empty a rigid container. The memory-based LWPLS and the non-memory-based LWPR algorithms [1,2,3], as well as both the batch and the incremental versions of GMM/GMR [4,5] were implemented, their comparison leading to very similar results, with the same pattern as regards to both the involved robot joints and the different initial experimental conditions. Tests where the teacher was instructed to follow a strategy compared to others where he was not lead to useful conclusions that permit devising the...

• Type: Journal
• Year: 2010
• Where: CCIA
• Authors: Leonel Rozo, Pablo Jiménez, Carme Torras
• Keywords: Artificial Intelligence, Autonomous Robot Rehearsal, CCIA 2010, Haptic Device, Robot