PhD defense - Adrien Matricon (in French)
Channel: Inria Flowers
Date: 13/06/2018

Merging Robotic Skills into More General Skills

The discovery of sensorimotor contingencies and their structuration into skills are both important topics in the field of robotics. In particular, robots need the ability to learn skills which are both semantically rich and as general as possible.

During this thesis, we studied the question of merging robotic skills into more general skills. After formally defining the notions that can be found in the litterature of skills and parameterized skills, we established a link between paramaterized skills and inverse models, then mirrored the dualism between forward and inverse models to propose a dual type of parameterized skills: forward-parameterized skills.

We went on to determine when merging skills into a forward-parameterized skill is relevant and when it’s not. The problem is then formulated as a regression problem, and algorithms inspired by the Occam Razor principle are proposed to find a mixture of experts that solves it with minimal complexity. Those algorithms are then applied to simulated object-manipulation data.