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English(EN) Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment

新方法增强VLM到VLA的适应性以实现机器人控制 · 跟踪2个来源

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新的研究论文提出了将视觉语言模型（VLM）适配为机器人视觉语言动作（VLA）模型的方法。第一篇论文介绍了CLAP（因果语言-动作预测），它将自然语言描述添加到动作序列中，以在微调过程中保持VLM的能力。第二篇论文Anchor-Align使用表示锚定和语言-动作对齐来防止预训练表示被覆盖并提高泛化能力。两种方法在机器人基准测试和物理机器人测试中都显示出显著的改进。 AI

影响 这些方法可以通过改善大型语言模型知识的迁移，从而实现更强大、更具泛化能力的机器人代理。

排序理由 两篇在arXiv上发表的学术论文，提出了用于将VLM适配为VLA的新方法。

在 arXiv cs.CV 阅读 →

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新方法增强VLM到VLA的适应性以实现机器人控制 · 跟踪2个来源

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两篇在arXiv上发表的学术论文，提出了用于将VLM适配为VLA的新方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura ·

    CLAP：通过语言-动作对齐实现直接的VLM到VLA适应

    arXiv:2607.08974v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult…

  2. arXiv cs.CV TIER_1 English(EN) · Dwip Dalal, Shivansh Patel, Chahit Jain, Jeonghwan Kim, Utkarsh Mishra, Alex Baratian, Hyeonjeong Ha, Heng Ji, Svetlana Lazebnik, Unnat Jain ·

    通过表示锚定和语言-动作对齐实现可泛化的VLA微调

    arXiv:2607.13429v1 Announce Type: cross Abstract: Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. However, BC finetuning progressively overwrites the pr…

  3. arXiv cs.CV TIER_1 English(EN) · Unnat Jain ·

    通过表示锚定和语言-动作对齐实现可泛化的VLA微调

    Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. However, BC finetuning progressively overwrites the pretrained representations that support visual and s…

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