Robot motion distributions often exhibit multi-modality and require flexible generative models for accurate representation. Streaming Flow Policies (SFPs) have recently emerged as a powerful paradigm for generating robot trajectories by integrating learned velocity fields directly in action space, enabling smooth and reactive control. However, existing formulations lack mechanisms for adapting trajectories post-training to enforce safety and task-specific constraints. We propose Constraint-Aware Streaming Flow (CASF), a framework that augments streaming flow policies with constraint-dependent metrics that reshape the learned velocity field during execution. CASF models each constraint, defined in either the robot's workspace or configuration space, as a differentiable distance function that is converted into a local metric and pulled back into the robot's control space. Far from restricted regions, the resulting metric reduces to the identity; near constraint boundaries, it smoothly attenuates or redirects motion, effectively deforming the underlying flow to maintain safety. This allows trajectories to be adapted in real time, ensuring that robot actions respect joint limits, avoid collisions, and remain within feasible workspaces, while preserving the multi-modal and reactive properties of streaming flow policies. We demonstrate CASF in simulated and real-world manipulation tasks, showing that it produces constraint-satisfying trajectories that remain smooth, feasible, and dynamically consistent, outperforming standard post-hoc projection baselines.

翻译：机器人运动分布常呈现多模态特性，需要灵活的生成模型进行精确表征。流式流策略（SFPs）作为一种新兴的强大范式，通过在动作空间中直接集成习得的速度场来生成机器人轨迹，实现了平滑且响应迅速的控制。然而，现有方法缺乏在训练后调整轨迹以强化安全性与任务特定约束的机制。本文提出约束感知流式流（CASF）框架，该框架通过引入约束相关度量来增强流式流策略，在执行过程中重塑习得的速度场。CASF将机器人的工作空间或构型空间中定义的每个约束建模为可微距离函数，该函数被转换为局部度量并拉回至机器人控制空间。在远离受限区域时，所得度量退化为单位度量；在约束边界附近，则平滑地衰减或重定向运动，从而有效变形底层流以维持安全性。这使得轨迹能够实时调整，确保机器人动作遵守关节限位、避免碰撞并保持在可行工作空间内，同时保留流式流策略的多模态与响应特性。我们在仿真与真实世界操作任务中验证了CASF，结果表明其生成的满足约束的轨迹保持平滑性、可行性与动态一致性，性能优于标准的后验投影基线方法。