操作策略终于能实时闭环了！πR²让大模型每40ms重新规划动作，比基线快4倍，成功率提升30%，特别适合动态环境。
通用操作策略多采用基于大预训练骨干的动作分块流策略，但开环运行导致无法反应中途输入，牺牲反应性，且重规划因感知-动作管线慢而受限。πR²基于扩散强制噪声调度，提出快慢通道分离（本体感觉每tick更新，视觉语言特征异步更新）和延迟自适应流调度，可在单步去噪内发出动作。在GR00T-N1.7上微调后，于真实xArm6+XHand平台实现约25Hz闭环重规划（每40ms响应一次观察），比基线快4倍。在仿真和真实操作任务中，πR²相较最强基线成功率分别提升23%和30%。
$π\mathbf{R}^2$: Reactive Real-time Flow Policies
Generalist manipulation policies increasingly take the form of action-chunking flow policies built on large pretrained backbones. Such chunks run open-loop, so the policy cannot react to sensory input arriving mid-execution, sacrificing \emph{reactivity}. Replanning more often would restore it, but the perception-to-action pipeline (a large backbone plus multiple denoising steps) is too slow: this \emph{latency} forbids frequent replanning and leaves committed actions stale, making such policies ill-suited for dynamic, closed-loop control. We present $π\mathbf{R}^2$, which makes these policies reactive and real-time while retaining large backbones, expressive multi-modal policies, and multi-action prediction. Built on the per-position noise schedule of diffusion forcing, $π\mathbf{R}^2$ contributes two ideas. First, it splits conditioning into a fast channel (proprioception, fresh every tick) and an asynchronously updated slow channel (vision-language features), so the policy reacts to proprioception within a chunk while tolerating stale vision. Second, a latency-adaptive flow schedule treats in-flight actions as inpainting conditioning and emits actions in one denoising step per call, letting one trained model adapt to varying hardware latency. Requiring minimal modification to existing architectures, $π\mathbf{R}^2$ can be finetuned from a pretrained policy: applied to GR00T-N1.7 on a real xArm6+XHand platform, it replans closed-loop roughly $4\times$ faster than the base policy (~$25$Hz on an A5000 GPU), acting on a fresh observation every $40$ms. Across simulation and real-world manipulation tasks, $π\mathbf{R}^2$ improves the success rate by up to $23\%$ in simulation and $30\%$ in the real world over the strongest baseline. Project page: https://pi-r2-flow.github.io/