Learning optimal switching policies for path tracking tasks on a mobile robot
نویسندگان
چکیده
A set of impedance controllers is used for both state estimation and tracking control on a mobile robot. State estimation is based on the states of a family of impedance controllers and tracking is implemented through a single controller from this set. Reinforcement learning techniques are used to create switching policies that optimize time or energy in a path tracking task.
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