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.
['Yunqing Wang', 'Bryan J. Thibodeau', 'Andrew H. Fagg', 'Roderic A. Grupen']
Learning optimal switching policies for path tracking tasks on a mobile robot