NDLI: Learning Generalizable Control Programs

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Learning Generalizable Control Programs

Content Provider	IEEE Xplore Digital Library
Author	Hart, S. Grupen, R.
Copyright Year	2009
Abstract	In this paper, we present a framework for guiding autonomous learning in robot systems. The paradigm we introduce allows a robot to acquire new skills according to an intrinsic motivation function that finds behavioral affordances. Affordances-in the sense of (Gibson, Toward and Ecological Psychology, Hillsdale, NJ, 1977)-describe the latent possibilities for action in the environment and provide a direct means of organizing functional knowledge in embodied systems. We begin by showing how a robot can assemble closed-loop action primitives from its sensory and motor resources, and then show how these primitives can be sequenced into multi-objective policies. We then show how these policies can be assembled hierarchically to support incremental and cumulative learning. The main contribution of this paper demonstrates how the proposed intrinsic motivator for affordance discovery can cause a robot to both acquire such hierarchical policies using reinforcement learning and then to generalize these policies to new contexts. As the framework is described, its effectiveness and applicability is demonstrated through a longitudinal learning experiment on a bimanual robot.
Starting Page	216
Ending Page	231
Page Count	16
File Size	1153994
File Format	PDF
ISSN	19430604
Volume Number	3
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Context Kinematics Manipulators Robot kinematics Navigation schema Cognitive architectures generalization incremental learning intrinsic motivation reinforcement learning
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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