Pre- and Post-Contact Policy Decomposition for Planar Contact Manipulation Under Uncertainty

Michael Koval, Nancy Pollard, and Siddhartha Srinivasa
Robotics: Science and Systems Conference, July, 2014.


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Abstract
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.

Notes

Text Reference
Michael Koval, Nancy Pollard, and Siddhartha Srinivasa, "Pre- and Post-Contact Policy Decomposition for Planar Contact Manipulation Under Uncertainty," Robotics: Science and Systems Conference, July, 2014.

BibTeX Reference
@inproceedings{Koval_2014_7599,
   author = "Michael Koval and Nancy Pollard and Siddhartha Srinivasa",
   title = "Pre- and Post-Contact Policy Decomposition for Planar Contact Manipulation Under Uncertainty",
   booktitle = "Robotics: Science and Systems Conference",
   month = "July",
   year = "2014",
}