GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
Paper recorded by Signals 4 on 2026-09-17 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-17 on arXiv · recorded by Signals 4 on 2026-09-18
Category: cs.AI · 人工智能 · first seen 2026-09-18
Abstract
Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fixed action horizon. During a rollout, different task stages may require different levels of action continuity, control precision, and closed-loop feedback, making a fixed horizon unable to accommodate changing control requirements. We propose \textbf
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Cite this page: GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies: the #10 most recent of 300 cs.AI papers we have recorded (as of 2026-09-17). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/geoaac-geometry-based-adaptive-action-chunking-from-denoising-trajectories-in-vl.html
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