Proceedings:
Learning Grounded Representations
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Papers from the 2001 AAAI Spring Symposium
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Abstract:
We present an experiment in sequential visual servo control of a dynamic manipulation task with unknown equations of motion and feedback from an uncalibrated camera. Our algorithm constructs a model of a Markov decision process (MDP) by means of grounding states in observed trajectories, and uses the model to find a control policy based on visual input, which maximizes a prespecified optimal control criterion balancing performance and control effort.
Spring
Papers from the 2001 AAAI Spring Symposium