arXiv:2510.01711v3 Announce Type: replace-cross Abstract: 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 s
The rapid advancement of Vision-Language Models (VLMs) and the increasing demand for robust robot manipulation in complex environments necessitate improved representation learning for Vision-Language-Action (VLA) models.
Improving VLA models' understanding of robotic specific signals like actions and proprioception is crucial for deploying more capable and autonomous robots, impacting industries from manufacturing to logistics.
This development proposes a method to bridge the gap between abstract VLM representations and concrete robotic states, potentially enabling more precise control and adaptability in robotic systems.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · Companies relying on less autonomous, human-supervised robotic systems
More capable and versatile VLA models will accelerate the development of autonomous robot manipulation tasks.
Enhanced robot capabilities could lead to increased automation across various industries, displacing some manual labor while creating new roles in robot management and development.
The widespread deployment of highly capable robots could transform supply chains, manufacturing processes, and even domestic labor markets, leading to significant economic and societal restructuring.
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Read at arXiv cs.LG