Journal
ARTIFICIAL INTELLIGENCE
Volume 172, Issue 4-5, Pages 392-412Publisher
ELSEVIER
DOI: 10.1016/j.artint.2007.07.003
Keywords
robot behavior; sensory-motor function; skill; planning; learning
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This article describes a system, called ROBEL, for defining a robot controller that learns from experience very robust ways of performing a high-level task such as navigate to. The designer specifies a collection of skills, represented as hierarchical tasks networks, whose primitives are sensory-motor functions. The skills provide different ways of combining these sensory-motor functions to achieve the desired task. The specified skills are assumed to be complementary and to cover different situations. The relationship between control states, defined through a set of task-dependent features, and the appropriate skills for pursuing the task is learned as a finite observable Markov decision process (MDP). This MDP provides a general policy for the task; it is independent of the environment and characterizes the abilities of the robot for the task. (c) 2007 Elsevier B.V. All rights reserved.
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