Modeling and learning robot manipulation strategies

Jiming Liu, Y. Y. Tang, Oussama Khatib

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


This paper describes a general approach to learning and planning robot manipulation strategies. Here, the strategies are represented using a discrete-event dynamical systems model where each node corresponds to a state in the robot task environment that triggers certain action schemata and each arc corresponds to a plausible action that brings the task environment into a new state. With such a representation, a manipulation strategy plan can be derived by searching a connected state transition path that is the most reliable. Here, we define the notion of reliability in terms of the estimated chance of success in reaching a desirable state. In the paper, we first present the formalism of discrete-event dynamical system in the context of robot manipulation tasks. Throughout the paper, we provide both illustrative and experimental examples to demonstrate the proposed approach.
Original languageEnglish
Title of host publicationExperimental Robotics V
Subtitle of host publicationThe Fifth International Symposium Barcelona, Catalonia, June 15-18, 1997
EditorsAlicia Casals, Anibal T. Almeida
PublisherSpringer Berlin Heidelberg
Number of pages14
ISBN (Electronic)9783540409205
Publication statusPublished - 15 Jun 1997
Event5th International Symposium on Experimental Robotics, ISER 1997 - Barcelona, Catalonia, Spain
Duration: 15 Jun 199718 Jun 1997 (Link to conference proceedings)

Publication series

NameLecture Notes in Control and Information Sciences
ISSN (Print)0170-8643
ISSN (Electronic)1610-7411


Symposium5th International Symposium on Experimental Robotics, ISER 1997
CityBarcelona, Catalonia
Internet address


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