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.

翻译：本文提出模型预测树（MPT），一种通过高效复用信息来提升性能的滚动时域树搜索算法。现有求解器仅复用前次迭代中质量最高的轨迹作为"热启动"，而我们的方法复用整个最优子树，使搜索能同时避开低质量区域并导向高质量区域。通过分析时变动力学下引入的跟踪误差，我们刻画了树复用的约束条件，揭示了搜索深度与动态变化时间尺度之间的权衡关系。数值研究表明，本算法在采用热启动的采样型交叉熵方法中表现优异。我们在自动驾驶车辆测试平台上展示了该规划器执行非抓取式操作任务的能力：在障碍物场中推动目标物体。相关代码发布于 https://github.com/jplathrop/mpt。