Safe and computationally efficient local planning for mobile robots in dense, unstructured human crowds remains a fundamental challenge. Moreover, ensuring that robot trajectories are similar to how a human moves will increase the acceptance of the robot in human environments. In this paper, we present Crowd-FM, a learning-based approach to address both safety and human-likeness challenges. Our approach has two novel components. First, we train a Conditional Flow-Matching (CFM) policy over a dataset of optimally controlled trajectories to learn a set of collision-free primitives that a robot can choose at any given scenario. The chosen optimal control solver can generate multi-modal collision-free trajectories, allowing the CFM policy to learn a diverse set of maneuvers. Secondly, we learn a score function over a dataset of human demonstration trajectories that provides a human-likeness score for the flow primitives. At inference time, computing the optimal trajectory requires selecting the one with the highest score. Our approach improves the state-of-the-art by showing that our CFM policy alone can produce collision-free navigation with a higher success rate than existing learning-based baselines. Furthermore, when augmented with inference-time refinement, our approach can outperform even expensive optimisation-based planning approaches. Finally, we validate that our scoring network can select trajectories closer to the expert data than a manually designed cost function.

翻译：在密集、非结构化人群环境中实现移动机器人的安全且计算高效的局部规划仍是一个根本性挑战。此外，确保机器人轨迹与人类移动方式相似将提升机器人在人类环境中的接受度。本文提出Crowd-FM，一种基于学习的方法以同时应对安全性与类人性挑战。我们的方法包含两个新颖组件：首先，我们在最优控制轨迹数据集上训练条件流匹配（CFM）策略，以学习机器人在任意给定场景下可选择的碰撞规避运动基元。所选最优控制求解器能生成多模态无碰撞轨迹，使CFM策略能够学习多样化的机动动作。其次，我们在人类示范轨迹数据集上训练评分函数，为流基元提供类人性评分。在推理阶段，通过选择评分最高的轨迹实现最优轨迹计算。实验表明，仅使用CFM策略即可实现比现有基于学习的基线方法更高成功率的无碰撞导航，从而改进了当前技术水平。此外，当结合推理时优化机制时，本方法甚至能超越计算昂贵的基于优化的规划方法。最后，我们验证了评分网络选择的轨迹比人工设计成本函数更接近专家数据。