Data Efficient Direct Policy Search For Manipulation Actions Using Bayesian Optimisation

A challenge in robot manipulation is how to learn tasks efficiently. We combine learning from demonstration with data efficient exploration guided by Bayesian optimisation. We use dynamic movement primitives to encode manipulation actions. These permit temporal and spatial scaling of the demonstrated trajectory. We demonstrate the effectiveness of direct policy search for the scaling parameters with Bayesian optimisation (BO). We evaluate BO against random search on two real robot tasks: a ‘throw object to target’ task, and a ‘flip object to target’ task. We are able to obtain good policy parameters despite large amounts of noise and a weak relationship between the parameters and the policy score.