Efficient Object Manipulation Planning with Monte Carlo Tree Search

TL;DR

This paper introduces an efficient object manipulation planning method combining Monte Carlo Tree Search with learned policy-value networks and heuristics, demonstrating improved success rates in simulations and real hardware.

Contribution

It presents a novel integration of MCTS with learned policies and heuristics for scalable and efficient object manipulation planning.

Findings

Significantly improves planning success rate.

Scales favorably for long manipulation sequences.

Demonstrates effectiveness in both simulation and real hardware.

Abstract

This paper presents an efficient approach to object manipulation planning using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient ADMM-based trajectory optimization algorithm to evaluate the dynamic feasibility of candidate contact sequences. To accelerate MCTS, we propose a methodology to learn a goal-conditioned policy-value network to direct the search towards promising nodes. Further, manipulation-specific heuristics enable to drastically reduce the search space. Systematic object manipulation experiments in a physics simulator and on real hardware demonstrate the efficiency of our approach. In particular, our approach scales favorably for long manipulation sequences thanks to the learned policy-value network, significantly improving planning success rate.