Pre- and post-contact policy decomposition for planar contact manipulation under uncertainty

—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 variableresolution discretization of the state space to solve for a postcontact 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 ac...

• Type: Journal
• Year: 2016
• Where: IJRR
• Authors: Michael C. Koval, Nancy S. Pollard, Siddhartha S. Srinivasa
• Keywords: IJRR 2016