Carnegie Mellon University
The
Configuration Lattices for Planar Contact Manipulation Under Uncertainty

Michael Koval, David Hsu, Nancy Pollard , and Siddhartha Srinivasa
Workshop on the Algorithmic Foundations of Robotics, December, 2016.


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Abstract
This work addresses the challenge of a robot using real-time feedback from contact sensors to reliably manipulate a movable object on a cluttered tabletop. We formulate this task as a partially observable Markov decision process (POMDP) in the joint space of robot configurations and object poses. This formulation enables the robot to explicitly reason about uncertainty and all major types of kinematic constraints: reachability, joint limits, and collision. We solve the POMDP using DESPOT, a state-of-the-art online POMDP solver, by leveraging two key ideas for computational efficiency. First, we lazily construct a discrete lattice in the robot’s configuration space. Second, we guide the search with heuristics derived from an unconstrained relaxation of the problem. We empirically show that our approach outperforms several baselines on a simulated seven degree-of-freedom manipulator.

Keywords
manipulation under uncertainty, planning under uncertainty, tactile sensing

Notes
Sponsor: NASA, NSF, ONR, and the Toyota Motor Corporation
Associated Center(s) / Consortia: Field Robotics Center
Number of pages: 16

Text Reference
Michael Koval, David Hsu, Nancy Pollard , and Siddhartha Srinivasa, "Configuration Lattices for Planar Contact Manipulation Under Uncertainty," Workshop on the Algorithmic Foundations of Robotics, December, 2016.

BibTeX Reference
@inproceedings{Koval_2016_8245,
   author = "Michael Koval and David Hsu and Nancy {Pollard } and Siddhartha Srinivasa",
   title = "Configuration Lattices for Planar Contact Manipulation Under Uncertainty",
   booktitle = "Workshop on the Algorithmic Foundations of Robotics",
   month = "December",
   year = "2016",
}