Reinforcement learning for imitating constrained reaching movements

Abstract

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. Here, we describe a system to teach the robot constrained reaching tasks. Our system is based on a dynamic 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, e.g., in the presence of obstacles.