Luke Shimanuki

Learning value functions with relational state representations for guiding task-and-motion planning

We propose a novel relational state representation and an action-value function learning algorithm that learns from planning experience for geometric task-and-motion planning (GTAMP) problems, in which the goal is to move several objects to regions in the presence of movable obstacles. The represent