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

arXiv: https://arxiv.org/abs/2411.15651 · HTML: https://mlsanity.com/paper/2411.15651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Value-Preserving Action Quantization](https://mlsanity.com/paper/2610.17252/) (2026)

## Abstract (arXiv)

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.