Robotics: Science and Systems XVI

Controlling Contact-Rich Manipulation Under Partial Observability

Florian Wirnshofer, Philipp Sebastian Schmitt, Georg von Wichert, Wolfram Burgard


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.



    AUTHOR    = {Florian Wirnshofer AND Philipp Sebastian Schmitt AND Georg von Wichert AND Wolfram Burgard}, 
    TITLE     = {{Controlling Contact-Rich Manipulation Under Partial Observability}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2020}, 
    ADDRESS   = {Corvalis, Oregon, USA}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2020.XVI.023}