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 new research stages, where the taught motion will be refined by autonomous robot rehearsal through reinforcement learning.
CitacióRozo, L.; Jimenez, P.; Torras, Carme. Learning force-based robot skills from haptic demonstration. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development núm. 220". Espluga de Francolí: IOS Press, 2010, p. 331-341.