让视觉语言动作模型更懂机器人状态，提升操控成功率
Contrastive Representation Regularization for Vision-Language-Action Models
- 用机器人本体感知状态的相对距离做软监督，增强控制相关表征
- 在真实机器人任务中将成功率从45.0%提升至58.3%
- 无需改动训练流程，适配现有VLA模型，轻量高效
视觉-语言-动作（VLA）模型通过利用预训练视觉-语言模型（VLM）的丰富表征，在机器人操作任务中展现出强大能力。然而，其表征仍不理想，对控制动作和本体感知信息不够敏感。为此，我们提出机器人状态感知对比损失（RS-CL），一种简单有效的表征正则化方法，旨在弥合VLM表征与机器人信号之间的差距。具体而言，RS-CL利用状态间的相对距离作为软监督信号，使表征更贴近机器人的本体感知状态。该方法补充原有动作预测目标，增强了控制相关的表征学习，且轻量、完全兼容标准VLA训练流程。实验表明，RS-CL显著提升现有先进VLA模型性能：在RoboCasa-Kitchen基准上达到69.7%的最高水平，并在具有挑战性的真实机器人操作任务中将成功率从45.0%提升至58.3%。
原文摘要 · Abstract (English)
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. Complementing the original action prediction objective, RS-CL enhances control-relevant representation learning, while being lightweight and fully compatible with standard VLA training pipelines. Our empirical results demonstrate that RS-CL substantially improves the performance of state-of-the-art VLA models; it pushes the prior art to 69.7% achieving the state-of-the-art performance on the RoboCasa-Kitchen benchmark, and boosts success rates from 45.0% to 58.3% on challenging real-robot manipulation tasks.
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