Meta Value Learning for Fast Policy-Centric Optimal Motion Planning

This paper considers policy-centric optimal motion planning with limited reaction time. The motion planning queries are determined by their goal regions and cost functionals, and are generated over time from a distribution. Once a new query is requested, the robot needs to quickly generate a motion planner which can steer the robot to the goal region while minimizing a cost functional. We develop a meta-learning-based algorithm to compute a meta value function, which can be fast adapted using a small number of samples of a new query. Simulations on a unicycle are conducted to evaluate the developed algorithm and show the anytime property of the proposed algorithm.