Learning Robust Plans for Mobile Robots from a Single Trial

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Reinforcement Learning

We address the problem of learning robust plans for robot navigation by observing particular robot behaviors. In this paper we present a method which can learn a robust reactive plan from a single example of a desired behavior. The system operates by translating a sequence of events arising from the eflector system into a plan which represents the dependencies among such events. This method allows us to rely on the underlying stability properties of low-level behavior processes in order to produce robust plans. Since the resultant plan reproduces the original behavior of the robot at a high level, it generalizes over small environmental changes and is robust to sensor and eflector noise.