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We propose a novel Parallel Monte Carlo tree search with Batched Simulations(PMBS) algorithm for accelerating long-horizon, episodic robotic planningtasks. Monte Carlo tree search (MCTS) is an effective heuristic searchalgorithm for solving episodic decision-making problems whose underlying searchspaces are expansive. Leveraging a GPU-based large-scale simulator, PMBSintroduces massive parallelism into MCTS for solving planning tasks through thebatched execution of a large number of concurrent simulations, which allows formore efficient and accurate evaluations of the expected cost-to-go over largeaction spaces. When applied to the challenging manipulation tasks of objectretrieval from clutter, PMBS achieves a speedup of over $30\times$ with animproved solution quality, in comparison to a serial MCTS implementation. Weshow that PMBS can be directly applied to real robot hardware with negligiblesim-to-real differences. Supplementary material, including video, can be foundat https://github.com/arc-l/pmbs.

Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning