Robot Motion Planning by Reusing Multiple Knowledge under Uncertain Conditions

This paper proposes a method for planning robot motions by integrating multiple knowledge that is effective in task achievement. The method efficiently obtains a new policy, which is a mapping from states to actions, on the basis of the knowledge presented in a state-action map. However, in some states, the applied knowledge fails to achieve a given task. In our method, the failing states are found by using the decrease in the state values, and the policy for these states is then modified. In order to demonstrate the validity of our method, we applied it to rearrangement tasks of multiple objects. The appropriate policies were obtained by integrating programs for similar tasks and a simple rule for the task process; moreover, a new knowledge that is effective in the rearrangement tasks was extracted from the obtained policies