Robotics: Science and Systems X
Pre- and Post-Contact Policy Decomposition for Planar Contact Manipulation Under Uncertainty
Michael Koval, Nancy Pollard, Siddhartha SrinivasaAbstract:
We consider the problem of using real-time feedback from contact sensors to create closed-loop pushing actions. To do so, we formulate the problem as a partially observable Markov decision process (POMDP) with a transition model based on a physics simulator and a reward function that drives the robot towards a successful grasp. We demonstrate that it is intractable to solve the full POMDP with traditional techniques and introduce a novel decomposition of the policy into pre- and post-contact stages to reduce the computational complexity. Our method uses an offline point-based solver on a variable-resolution discretization of the state space to solve for a post-contact policy as a pre-computation step. Then, at runtime, we use an A* search to compute a pre-contact trajectory. We prove that the value of the resulting policy is within a bound of the value of the optimal policy and give intuition about when it performs well. Additionally, we show the policy produced by our algorithm achieves a successful grasp more quickly and with higher probability than a baseline policy.
Bibtex:
@INPROCEEDINGS{Koval-RSS-14, AUTHOR = {Michael Koval AND Nancy Pollard AND Siddhartha Srinivasa}, TITLE = {Pre- and Post-Contact Policy Decomposition for Planar Contact Manipulation Under Uncertainty}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2014}, ADDRESS = {Berkeley, USA}, MONTH = {July}, DOI = {10.15607/RSS.2014.X.034} }