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 that can be accomplished by everyone. 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 with a learned speed trajectory and combined with a reinforcement learning module to allow the robot to adapt the trajectory when facing a new situation, such as avoiding obstacles.