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arXiv · 2411.15651

Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search

John Lathrop·Benjamin Rivi`ere·Jedidiah Alindogan·Soon-Jo Chung
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Abstract

We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away from the low-quality areas and towards the high-quality areas. We characterize the restrictions on tree reuse by analyzing the induced tracking error under time-varying dynamics, revealing a tradeoff between the search depth and the timescale of the changing dynamics. In numerical studies, our algorithm outperforms state-of-the-art sampling-based cross-entropy methods with hotstarting. We demonstrate our planner on an autonomous vehicle testbed performing a nonprehensile manipulation task: pushing a target object through an obstacle field. Code associated with this work will be made available at https://github.com/jplathrop/mpt.

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John Lathrop, Benjamin Rivi`ere, Jedidiah Alindogan, Soon-Jo Chung. 2024-11-23. Model Predictive Trees: Sample-Efficient Receding Horizon Planning with Reusable Tree Search. https://arxiv.org/abs/2411.15651

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