Efficient Object Manipulation Planning with Monte Carlo Tree Search

Huaijiang Zhu, Ludovic Righetti

TL;DR: This work presents an efficient approach to object manipulation planning using Monte Carlo Tree Search and trajectory optimization.

Abstract: This work 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 used 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 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2206.09023/code)