Unobservable Monte Carlo Planning for Nonprehensile Rearrangement Tasks

Abstract

In this work, we present an anytime planner for creating open-loop trajectories that solve rearrangement planning problems under uncertainty using nonprehensile manipulation. We first extend the Monte Carlo Tree Search algorithm to the unobservable domain. We then propose two default policies that allow us to quickly determine the potential to achieve the goal while accounting for the contact that is critical to rearrangement planning. The first policy uses a learned model generated from a set of user demonstrations. This model can be quickly queried for a sequence of actions that attempts to create contact with objects and achieve the goal. The second policy uses a heuristically guided planner in a subspace of the full state space. Using these goal informed policies, we are able to find initial solutions to the problem quickly, then continuously refine the solutions as time allows. We demonstrate our algorithm on a 7 degree-of-freedom manipulator moving objects on a table.