Action-to-Action Flow Matching

近年来，基于扩散的策略通过把动作预测建模为条件去噪过程，在机器人领域取得了显著成功。然而，从随机高斯噪声采样通常需要多次迭代才能得到干净动作，推理延迟因此成为实时控制的主要瓶颈。本文质疑这种无信息噪声采样是否必要，并提出 Action-to-Action Flow Matching（A2A）：用先前本体动作提供的信息初始化生成过程，而不是从随机噪声开始。不同于把本体反馈仅当作静态条件的已有方法，A2A 利用历史本体动作序列，并将其嵌入高维 latent 空间作为动作生成的起点。这个设计绕开昂贵的迭代去噪，同时捕捉机器人的物理动力学与时间连续性。大量实验显示，A2A 具有更高训练效率、更快推理速度和更强泛化能力；它在最低单步推理下仍能生成高质量动作，并对视觉扰动和未见配置更鲁棒。作者还把 A2A 扩展到视频生成，展示了它在时间建模任务中的通用性。

Diffusion-based policies have recently achieved remarkable success in robotics by formulating action prediction as a conditional denoising process. However, the standard practice of sampling from random Gaussian noise often requires multiple iterative steps to produce clean actions, leading to high inference latency that incurs a major bottleneck for real-time control. In this paper, we challenge the necessity of uninformed noise sampling and propose Action-to-Action flow matching (A2A), a novel policy paradigm that shifts from random sampling to initialization informed by the previous proprioceptive action. Unlike existing methods that treat proprioceptive action feedback as static conditions, A2A leverages historical proprioceptive sequences, embedding them into a high-dimensional latent space as the starting point for action generation. This design bypasses costly iterative denoising while effectively capturing the robot's physical dynamics and temporal continuity. Extensive experiments demonstrate that A2A exhibits high training efficiency, fast inference speed, and improved generalization. Notably, A2A enables high-quality action generation in as few as a single inference step, and exhibits superior robustness to visual perturbations and enhanced generalization to unseen configurations. Lastly, we also extend A2A to video generation, demonstrating its broader versatility in temporal modeling.