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Abstract

This paper explores the planning and control of a manipulation task accomplished in conditions of high uncertainty. Statistical techniques, like particle filters, provide a framework for expressing the uncertainty and partial observability of the real world and taking actions to reduce them. We explore a classic manipulation problem of planar batting, but with a new twist of shape, pose and impact uncertainty. We demonstrate a technique for characterizing and reducing this uncertainty using a particle filter coupled with a lookahead planner that maximizes information gain. We show that a twostep planner that first acts for information gain and then acts to maximize the expectation of achieving a desired goal is effective at managing shape, pose and impact uncertainty.

Notes

Sponsor: NSF, Intel Summer Research Fellowship

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Personal Robotics

Number of pages: 7

Text Reference

Jiaxin Fu, Siddhartha Srinivasa, Nancy Pollard, and Bart Nabbe, "Planar batting under shape, pose, and impact uncertainty," IEEE International Conference on Robotics and Automation (ICRA), April, 2007.

BibTeX Reference

@inproceedings{Fu_2007_5685,
   author = "Jiaxin Fu and Siddhartha Srinivasa and Nancy Pollard and Bart Nabbe",
   title = "Planar batting under shape, pose, and impact uncertainty",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "April",
   year = "2007",
}

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