Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
Diffusion 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.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
21%
View original evidence
relative gains in average success rate of 21% over Diffusion Forcing in matched guidance-based planning
Open source S4
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
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Automatically extracted; no manual editorial approval recorded.
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
Open source S4
diction horizons and history lengths on LIBERO-10 under the official reset protocol. In unified video-action prediction, Diffusion ReRoll improves policy and inverse dynamics performance, especially under out-of-distribution evaluation, and achieves the best action-video consistency. Real-world navigation further demonstrates improved success under visual distribution shifts with onboard inference. These results show
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org