融合生成与优化，让机器人在动态环境中更安全高效导航。
Unified Generation-Refinement Planning: Bridging Guided Flow Matching and Sampling-Based MPC for Social Navigation
- 用条件流匹配生成多样轨迹，再由模型预测控制优化
- 实测在安全、任务完成率和计算时间上均优于单一方法
- 适合需要实时适应的智能机器人导航场景
在动态、以人为主的环境中实现鲁棒机器人规划仍具挑战，源于多模态不确定性、实时适应需求及安全约束。基于优化的规划器虽能显式处理约束，但对初始化敏感且在动态环境下表现不佳；基于学习的规划器能自然捕捉多模态解空间，却常无法保证约束满足。本文提出统一的生成-精炼框架，结合奖励引导的条件流匹配（CFM）与模型预测路径积分（MPPI）控制。核心思想是生成与优化之间的双向信息交换：奖励引导的CFM为MPPI提供多样且有信息量的轨迹先验，而优化后的MPPI轨迹又作为下一轮CFM生成的热启动。以自主社交导航为应用场景，实验表明该方法在安全性、任务性能与计算时间之间取得更好平衡，并可实时适应动态环境。代码已公开于 https://cfm-mppi.github.io。
原文摘要 · Abstract (English)
Robust robot planning in dynamic, human-centric environments remains challenging due to multimodal uncertainty, the need for real-time adaptation, and safety requirements. Optimization-based planners enable explicit constraint handling but can be sensitive to initialization and struggle in dynamic settings. Learning-based planners capture multimodal solution spaces more naturally, but often lack reliable constraint satisfaction. In this paper, we introduce a unified generation-refinement framework that combines reward-guided conditional flow matching (CFM) with model predictive path integral (MPPI) control. Our key idea is a bidirectional information exchange between generation and optimization: reward-guided CFM produces diverse, informed trajectory priors for MPPI refinement, while the optimized MPPI trajectory warm-starts the next CFM generation step. Using autonomous social navigation as a motivating application, we demonstrate that the proposed approach improves the trade-off between safety, task performance, and computation time, while adapting to dynamic environments in real-time. The source code is publicly available at https://cfm-mppi.github.io.
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