English(EN) CondenseVLA: Learnable History Condensation for Efficient Multi-Frame VLA

CondenseVLA通过可学习的历史压缩提高了VLA的效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了CondenseVLA，一种用于视觉语言代理（VLA）的新颖方法，可高效处理多帧视觉上下文。该方法使用可学习的解码器端历史压缩模块，将观察历史提炼为固定数量的槽令牌，与简单地扩大历史窗口相比，降低了计算成本和冗余。CondenseVLA在Simpler-WidowX、LIBERO和依赖记忆的RMBench任务等各种任务中都显示出成功率的显著提高，尽管在更复杂的多观察场景中仍面临挑战。 AI

影响通过更好地利用历史视觉数据，提高了视觉语言代理的效率和性能。

排序理由关于视觉语言代理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Feiyang Hong, Yujie Wei, Bo Zhao, Xiu Su, Hongxun Yao, Shuo Yang · 2026-10-21 04:00

CondenseVLA：可学习的历史压缩，实现高效多帧VLA

arXiv:2610.25927v2 Announce Type: replace Abstract: Robot manipulation often depends on short-term cues - such as brief contacts, temporary occlusions, and intermediate object states - that are ambiguous in a single frame and can only be resolved by integrating observations over …

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CondenseVLA：可学习的历史压缩，实现高效多帧VLA

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