Controlling Contact-Rich Manipulation Under Partial Observability

Abstract

In this paper, we present an integrated, model-based system for state estimation and control in dynamic manipulation tasks with partial observability. We track a belief over the system state using a particle filter from which we extract a Gaussian Mixture Model (GMM). This compressed representation of the belief is used to automatically create a discrete set of goal-directed motion controllers. A reinforcement learning agent then switches between these motion controllers in real-time to accomplish the manipulation task. The proposed system closes the loop from joint sensor feedback to high-frequency, acceleration-limited position commands, thus eliminating the need for pre-and post-processing. We evaluate our approach with respect to five distinct manipulation tasks from the domains of active localization, grasping under uncertainty, assembly, and non-prehensile object manipulation. Extensive simulations demonstrate that the hierarchical policy actively exploits the uncertainty information encoded in the compressed belief. Finally, we validate the proposed method on a real-world robot.