Reliable goal-directed reactive control of autonomous mobile robots
Reliable goal-directed reactive control of autonomous mobile robots
Case-based reasoning
Teleassistance: using deictic gestures to control robot action
Teleassistance: using deictic gestures to control robot action
Reacting, planning, and learning in an autonomous agent
Machine intelligence 14
Autonomous Learning from the Environment
Autonomous Learning from the Environment
A Distributed Model for Mobile Robot Environment-Learning and Navigation
A Distributed Model for Mobile Robot Environment-Learning and Navigation
Case-based planning: an integrated theory of planning, learning and memory
Case-based planning: an integrated theory of planning, learning and memory
Adaptive execution in complex dynamic worlds
Adaptive execution in complex dynamic worlds
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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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 effector 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 effector noise.