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Learning policies for contact-rich manipulation is a challenging problem due to the presence of multiple contact modes with different dynamics, which complicates state and action exploration. Contact-rich motion planning uses simplified dynamics to reduce the search space dimension, but the found plans are then difficult to execute under the true object-manipulator dynamics. This paper presents an algorithm for learning controllers based on guided policy search, where motion plans based on simplified dynamics define rewards and sampling distributions for policy gradient-based learning. We demonstrate that our guided policy search method improves the ability to learn manipulation controllers, through a task involving pushing a box over a step. 

In this work, we discuss the modeling, control, and implementation of a rimless wheel with a torso. We derive and compare two control methodologies: a discrete-time controller (DT) that updates the controls once-per-step and a continuous-time controller (CT) that updates gains continuously. For the discrete controller, we use least-squares estimation method to approximate the Poincare ́ map on a certain section and use discrete- linear-quadratic-regulator (DQLR) to stabilize a (closed-form) linearization of this map. For the continuous controller, we introduce moving Poincare ́ sections and stabilize the transverse dynamics along these moving sections. For both controllers, we estimate the region of attraction of the closed-loop system using sum-of-squares methods. Analysis of the impact map yields a refinement of the controller that stabilizes a steady-state walking gait with minimal energy loss. We present both simulation and experimental results that support the validity of the proposed approaches. We find that the CT controller has a larger region of attraction and smoother stabilization as compared with the DT controller.