Instance-Based Action Models for Fast Action Planning

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Instance-Based Action Models for Fast Action Planning
Abstract. Two main challenges of robot action planning in real domains are uncertain action effects and dynamic environments. In this paper, an instance-based action model is learned empirically by robots trying actions in the environment. Modeling the action planning problem as a Markov decision process, the action model is used to build the transition function. In static environments, standard value iteration techniques are used for computing the optimal policy. In dynamic environments, an algorithm is proposed for fast replanning, which updates a subset of state-action values computed for the static environment. As a test-bed, the goal scoring task in the RoboCup 4-legged scenario is used. The algorithms are validated in the problem of planning kicks for scoring goals in the presence of opponent robots. The experimental results both in simulation and on real robots show that the instance-based action model boosts performance over using parametric models as done previously, and also...
Mazda Ahmadi, Peter Stone
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Authors Mazda Ahmadi, Peter Stone
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