πR^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 reactivity. Replanning more often would restore it, but the perception-to-action pipeline (a large backbone plus multiple denoising steps) is too slow: this latency forbids frequent replanning and leaves committed actions stale, making such policies ill-suited fo

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ethics.ai (27 July 2026), “πR^2: Reactive Real-time Flow Policies,” evidence record 14903, https://ethics.ai/record/14903 (originally published by HuggingFace Daily Papers).

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