Reinforcement Learning for Imitating Constrained Reaching Movements

The goal of developing algorithms for programming robots by demonstration is to create an easy way of programming robots such that it can be accomplished by anyone. When a demonstrator teaches a task to a robot, he/she shows some ways of fulfilling the task, but not all the possibilities. The robot must then be able to reproduce the task even when unexpected perturbations occur. In this case, it has to learn a new solution. In this paper, we describe a system to teach to the robot constrained reaching tasks. Our system is based on a dynamical system generator modulated by a learned speed trajectory. This system is combined with a reinforcement learning module to allow the robot to adapt the trajectory when facing a new situation, for example in the presence of obstacles. keywords: Programming by Demonstration, Reinforcement Learning, Dynamical Systems, Gaussian Mixture Model.