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、非结构化的人群中为移动机器人实现安全且计算高效的局部规划，仍然是一个根本性挑战。此外，确保机器人轨迹与人类移动方式相似，将提高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，以同时应对安全性和类人性这两项挑战。我们的方法包含两个新颖的组成部分。首先，我们在最优控制轨迹数据集上训练一个条件流匹配策略，以学习一组机器人可在任何给定场景中选择的无碰撞运动基元。所选的最优控制求解器能够生成多模态的无碰撞轨迹，从而使CFM策略能够学习到多样化的机动动作。其次，我们在人类示范轨迹数据集上学习一个评分函数，该函数为流基元提供类人性评分。在推理时，计算最优轨迹需要选择评分最高的那一条。我们的方法改进了现有技术水平，实验表明，仅使用我们的CFM策略就能以比现有基于学习的基线更高的成功率实现无碰撞导航。此外，当结合推理时细化步骤，我们的方法甚至能超越计算成本高昂的基于优化的规划方法。最后，我们验证了我们的评分网络能够比手动设计的成本函数选择出更接近专家数据的轨迹。