LAUSR

An important challenge when using reinforcement learning for learning motions in robotics is the choice of parameterization for the policy. We use Gaussian Mixture Regression to extract a parameterization with relevant non-linear features from a set of demonstrations of a motion following the paradigm of learning from demonstration. The resulting parameterization takes the form of a non-linear time-invariant dynamical system (DS). We use this time-invariant DS as a parameterized policy for a variant of the PI2 policy search algorithm. This paper contributes by adapting PI2 for our time-invariant motion representation. We introduce two novel parameter exploration schemes that can be used to (1) sample model parameters to achieve a uniform exploration in state space and (2) explore while ensuring stability of the resulting motion model. Additionally, a state dependent stiffness profile is learned simultaneously to the reference trajectory and both are used together in a variable impedance control architecture. This learning architecture is validated in a hardware experiment consisting of a digging task using a KUKA LWR platform.