Objective-based stochastic framework for manipulation planning

Steven M. La Valle, Seth A. Hutchinson

Research output: Contribution to conferencePaperpeer-review

Abstract

We consider the problem of determining robot manipulation plans when sensing and control uncertainties are specified as conditional probability densities. Traditional approaches are usually based on worst-case error analysis in a methodology known as preimage backchaining. We have developed a general framework for determining sensor-based robot plans by blending ideas from stochastic optimal control and dynamic game theory with traditional preimage backchaining concepts. We argue that the consideration of a precise loss (or performance) functional is crucial to determining and evaluating manipulation plans in a probabilistic setting. We consequently introduce a stochastic, performance preimage that generalizes previous preimage notions. We also present some optimal strategies for planar manipulation tasks that were computer by a dynamic programming-based algorithm.

Original languageEnglish (US)
Pages1772-1779
Number of pages8
StatePublished - 1994
EventProceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems. Part 3 (of 3) - Munich, Ger
Duration: Sep 12 1994Sep 16 1994

Other

OtherProceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems. Part 3 (of 3)
CityMunich, Ger
Period9/12/949/16/94

ASJC Scopus subject areas

  • Engineering(all)

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