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Modeling and learning robot manipulation strategies

  • Chapter 14 Learning & Skill Acquisition
  • Conference paper
  • First Online:
Experimental Robotics V

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 232))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Computing Studies, Baptist University, 224 Waterloo Road, Kowloon Tong, Hong Kong

    Jiming Liu & Y. Y. Tang

  2. Robotics Laboratory, Department of Computer Science, Stanford University, CA, USA

    Oussama Khatib

Authors
  1. Jiming Liu
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  2. Y. Y. Tang
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  3. Oussama Khatib
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Editor information

Alicia Casals Anibal T. de Almeida

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© 1998 Springer-Verlag London Limited

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Liu, J., Tang, Y.Y., Khatib, O. (1998). Modeling and learning robot manipulation strategies. In: Casals, A., de Almeida, A.T. (eds) Experimental Robotics V. Lecture Notes in Control and Information Sciences, vol 232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0113002

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  • DOI: https://doi.org/10.1007/BFb0113002

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76218-8

  • Online ISBN: 978-3-540-40920-5

  • eBook Packages: Springer Book Archive

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