Anytime policy planning in large dynamic environments with interactive uncertainty

This paper addresses the problem of planning a policy in large environments where the actions of a robot affect the distribution of uncertainty in the environment. We focus on the problem of robot navigation through interactive crowds and present an anytime receding horizon technique that uses AO* together with small look-up table solutions. We demonstrate the feasibility of this technique in simulations with thousands of uncertain dynamic obstacles.We also investigate the importance of modeling uncertainty and interaction in this problem. We identify situations in which a naïve approach works well and characterize conditions under which our approach is needed.