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, as well as both the batch and the incremental versions of GMM/GMR 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 new research stages, where the taught motion will be refined by autonomous robot rehearsal through reinforcement learning.


intelligent robots, robot programming, telerobotics.

Author keywords

robot learning, LWL, GMM, GMR

Scientific reference

L. Rozo, P. Jiménez and C. Torras. Learning force-based robot skills from haptic demonstration, 13th Catalan Conference on Artificial Intelligence, 2010, Espluga de Francolí, Spain, in Artificial Intelligence Research and Development, Vol 220 of Frontiers in Artificial Intelligence and Applications, pp. 331-340, 2010, IOS Press.