[Submitted on 17 Sep 2026]
Title:Catch Me If You Can: Real-Time Feedback Denoising for Responsive VLAs
View a PDF of the paper titled Catch Me If You Can: Real-Time Feedback Denoising for Responsive VLAs, by Yiheng Ji and 3 other authors
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Abstract:Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by combining semantic knowledge from pretrained vision-language models with expressive action-generation policies. Diffusion-based action generators are particularly effective for modeling temporally coherent action chunks, but these chunks are typically executed open-loop after inference. This limits responsiveness when objects move, contacts change, or the scene evolves during execution. We propose VLA-Feedback, a two-timescale architecture that combines low-frequency diffusion planning with high-frequency visual feedback. Rather than fully denoising an action chunk before execution, VLA-Feedback retains its final denoising step as a lightweight feedback interface, allowing each action to be corrected using the latest observation before it is executed. This design preserves the expressiveness of the diffusion planner while enabling real-time action correction without rerunning the full vision-language diffusion model. VLA-Feedback matched GR00T on static LIBERO tasks while improving average success on dynamic simulation tasks from 27.5% to 85.0%. On real-robot tasks, it improved average success from 51% to 73%. Additional materials can be found on our project page: this https URL.
Comments: 10th Conference on Robot Learning (CoRL 2026), Austin TX, USA
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.21022 [cs.RO]
(or arXiv:2609.21022v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.21022
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yiheng Ji [view email] [v1] Thu, 17 Sep 2026 19:20:15 UTC (18,464 KB)
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