CLAP Adapts VLMs to VLAs with Language-Action Grounding.

This research introduces CLAP (Causal Language-Action Prediction), a novel approach for directly adapting powerful Vision-Language Models (VLMs) into Vision-Language-Action models (VLAs) with minimal architectural modifications. The primary challenge in this adaptation is the mismatch between the VLM's pretrained language distribution and the numerical action sequences required for robotic control. CLAP addresses this by conditioning the prediction of precise action tokens on a natural-language action plan. The method involves prepending each numeric action sequence with a corresponding natural language description, effectively guiding the VLM's generation towards the desired actions while preserving its core capabilities. With just a single epoch of fine-tuning, CLAP demonstrates significant performance gains, achieving 90.8% on the LIBERO benchmark and enhancing robustness against various perturbations. The researchers plan to release CLAP as an open-weight, multi-scale VLA family, facilitating further analysis of VLM-to-VLA capability transfer.