将动作轨迹视为流轨迹，实现机器人控制的实时动作流输出
Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories
- 用窄高斯分布从上一动作出发，逐步积分速度场生成动作序列
- 采样过程中即可实时输出动作，支持滚动时域执行，速度更快
- 保留多模态行为建模能力，适合需要快速反馈的机器人学习任务
扩散/流匹配策略虽能模仿复杂多模态动作轨迹，但计算开销大，需完整采样后才能执行动作。本文提出流式流策略，将动作轨迹直接视为流轨迹，不从纯噪声开始，而是从上一动作附近的窄高斯分布采样，并通过流匹配学习的速度场逐步积分，生成连续动作序列。该方法可在采样过程中实时输出动作，支持滚动时域策略执行。通过训练流模型使其稳定在示范轨迹附近，减少分布偏移，提升模仿学习性能。实验表明，流式流策略在保持多模态建模能力的同时，显著加快策略执行速度，实现更紧密的传感-运动闭环。
原文摘要 · Abstract (English)
Recent advances in diffusion$/$flow-matching policies have enabled imitation learning of complex, multi-modal action trajectories. However, they are computationally expensive because they sample a trajectory of trajectories: a diffusion$/$flow trajectory of action trajectories. They discard intermediate action trajectories, and must wait for the sampling process to complete before any actions can be executed on the robot. We simplify diffusion$/$flow policies by treating action trajectories as flow trajectories. Instead of starting from pure noise, our algorithm samples from a narrow Gaussian around the last action. Then, it incrementally integrates a velocity field learned via flow matching to produce a sequence of actions that constitute a single trajectory. This enables actions to be streamed to the robot on-the-fly during the flow sampling process, and is well-suited for receding horizon policy execution. Despite streaming, our method retains the ability to model multi-modal behavior. We train flows that stabilize around demonstration trajectories to reduce distribution shift and improve imitation learning performance. Streaming flow policy outperforms prior methods while enabling faster policy execution and tighter sensorimotor loops for learning-based robot control. Project website: https://streaming-flow-policy.github.io/
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