@inproceedings{e6f7821f47424481a12c6d9afd59f81c,
title = "Modeling and learning robot manipulation strategies",
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.",
author = "Jiming Liu and Tang, {Y. Y.} and Oussama Khatib",
note = "Publisher copyright: {\textcopyright} 1998 Springer-Verlag London Limited",
year = "1997",
month = jun,
day = "15",
doi = "10.1007/BFb0113002",
language = "English",
series = "Lecture Notes in Control and Information Sciences",
publisher = "Springer Berlin Heidelberg",
pages = "687--700",
editor = "Alicia Casals and Almeida, {Anibal T.}",
booktitle = "Experimental Robotics V",
edition = "1",
}