Learning how to combine sensory-motor functions into a robust behavior

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-...

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Bibliographic Details
Published in:Artificial intelligence 2008-03, Vol.172 (4), p.392-412
Main Authors: Morisset, Benoit, Ghallab, Malik
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Learning
Learning and adaptive systems
Planning
Robot behavior
Robotics
Sensory-motor function
Skill
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Summary: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.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2007.07.003