Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories

TL;DR

The paper introduces Streaming Flow Policy, a method that simplifies diffusion/flow-matching policies by treating action trajectories as flow trajectories, enabling real-time streaming of actions during policy execution.

Contribution

It proposes a novel approach that allows streaming actions during flow sampling, reducing computation and enabling faster, on-the-fly robot control while maintaining multi-modal behavior modeling.

Findings

Outperforms prior methods in imitation learning tasks.

Enables faster policy execution and real-time streaming of actions.

Retains the ability to model complex, multi-modal behaviors.

Abstract

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/