(Machine) Learning Robot Control Policies

It currently requires years of education and practice before a skilled user can successfully program a sophisticated robot platform to perform a given task. We are exploring ways in which statistical machine learning techniques can enable Learning from Demonstration, an approach where users ‘reprogram’ a robot without writing code. In this scenario, a user demonstrates the desired task and the robot learns to perform the task by observing its performance. We treat this learning as a form of Policy Transfer, where the decision making policy latent in the demonstrator is transitioned onto the robot.