Contrastive Representation Regularization for Vision-Language-Action Models

Understand

Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs).

• However, their representations arguably remain suboptimal, lacking sensitivity to robotic signals such as control actions and proprioceptive information.
• To address the issue, we introduce Robot State-aware Contrastive Loss (RS-CL), a simple and effective representation regularization for VLA models, designed to bridge the gap between VLM representations and robotic signals.
• In particular, RS-CL aligns the representations more closely with the robot's proprioceptive states by using relative distances between the states as soft supervision.