Using Reinforcement Learning to Adapt an Imitation Task
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 that allows a robot to re-learn constrained reaching tasks by combining the knowledge acquired during the demonstration, with that acquired though reinforcement learning.