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CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

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CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding
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Computer Science > Robotics arXiv:2607.08974 (cs) [Submitted on 9 Jul 2026] Title:CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding Authors:Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Directly converting pretrained VLMs into VLAs with minimal architectural change offers a more transparent path to understanding how VLM capabilities transfer across model scales. The core obstacle is output-distribution mismatch: predicting actions as bare numeric token sequences moves generation away from the VLM's pretrained language distribution, degrading the capabilities we seek to preserve.

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Computer Science > Robotics arXiv:2607.08974 (cs) [Submitted on 9 Jul 2026] Title:CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding Authors:Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura View a PDF of the paper titled CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding, by Yuri Ishitoya and 5 other authors View PDF HTML (experimental) 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 to isolate what the VLM contributes to control.

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