Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
Overview
This research proposes Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons. By selectively re-noising regions that reach a predefined noise threshold, the method allows for iterative cross-horizon revision, improving performance in tasks like planning, policy learning, and navigation. The results show significant gains over existing methods in both simulated and real-world settings.
From arXiv Robotics — research abstracts
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Reported quantity · Reported success rate: 21 percent · Basis not reported
“learning, unified video-action modeling, and real-world navigation. On OGBench PointMaze and AntMaze, Diffusion ReRoll achieves relative gains in average success rate of 21% over Diffusion Forcing in matched guidance-based planning and 23% over Diffuser in goal-inpainting. In diffusion-policy-style action prediction, Diffusion ReRoll improves average success by 56.5% relative to Diffusion Policy across different pre”
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- 2026-10-30T23:02:41.836Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T23:02:26.899Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalDiffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
This research proposes Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons. By selectively re-noising regions that reach a predefined noise threshold, the method allows for iterative cross-horizon revision, improving performance in tasks like planning, policy learning, and navigation. The results show significant gains over existing methods in both simulated and real-world settings.
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