$π\mathbf{R}^2$: 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

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ethics.ai (28 July 2026), “$π\mathbf{R}^2$: Reactive Real-time Flow Policies,” evidence record 14417, https://ethics.ai/record/14417 (originally published by arXiv cs.AI).

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