Learning State Grounding for Optimal Visual Servo Control of Dynamic Manipulation

Daniel Nikovski and Illah Nourbakhsh

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.

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