Learning strategies and representations for intuitive robot learning from demonstration

Robots are becoming more and more present around us, both in industries and in our homes. One key capability of robots is their adaptability to various situations that might appear in the real world. Robot skill learning is therefore a crucial aspect of robotics aiming to provide robots with programs enabling them to perform one or several tasks successfully. While such programming is usually done by an engineer or a developer, making robot programming available to anyone would dramatically increase the range of applications currently feasible for robots. Learning from Demonstration (LfD) is a robot skill learning paradigm addressing this aim by developing intuitive frameworks for non-expert users to easily (re)program robots. While Learning from Demonstration has emerged as a successful way to program robots, several limitations remain to be addressed. Typical approaches still require some forms of preprocessing, such as the alignment of the demonstrations, or the choice of the movement representation. Also, the algorithms have to run with a relatively low number of demonstrations that human users are typically willing to give, while being performant, adaptable and generalizable to new situations. In this thesis, we propose to address these shortcomings with methods that make Learning from Demonstration more intuitive and user-friendly. We notably propose a novel movement representation requiring no demonstration alignment, and active learning strategies that permit to learn complex skills from fewer demonstrations.