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Published in:
2011 | OriginalPaper | Chapter
Stochastic Abstract Policies for Knowledge Transfer in Robotic Navigation Tasks
Authors : Tiago Matos, Yannick Plaino Bergamo, Valdinei Freire da Silva, Anna Helena Reali Costa
Published in: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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Most work in navigation approaches for mobile robots does not take into account existing solutions to similar problems when learning a policy to solve a new problem, and consequently solves the current navigation problem from scratch. In this article we investigate a knowledge transfer technique that enables the use of a previously know policy from one or more related source tasks in a new task. Here we represent the knowledge learned as a stochastic abstract policy, which can be induced from a training set given by a set of navigation examples of state-action sequences executed successfully by a robot to achieve a specific goal in a given environment. We propose both a probabilistic and a nondeterministic abstract policy, in order to preserve the occurrence of all actions identified in the inductive process. Experiments carried out attest to the effectiveness and efficiency of our proposal.