ROSA: Harnessing Robot States for Vision-Language and Action Alignment

• URL: http://arxiv.org/abs/2506.13679v1
• Date: Mon, 16 Jun 2025 16:34:20 GMT
• Title: ROSA: Harnessing Robot States for Vision-Language and Action Alignment
• Authors: Yuqing Wen, Kefan Gu, Haoxuan Liu, Yucheng Zhao, Tiancai Wang, Haoqiang Fan, Xiaoyan Sun,
• Abstract summary: Vision-Language Models (VLMs) have made significant advance in end-to-end robotic control.<n>We propose a novel training paradigm, ROSA, which leverages robot state estimation to improve alignment between vision-language and action spaces.
• Score: 24.426285156386715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Language Models (VLMs). A fundamental challenge in developing such models is effectively aligning the vision-language space with the robotic action space. Existing approaches typically rely on directly fine-tuning VLMs using expert demonstrations. However, this strategy suffers from a spatio-temporal gap, resulting in considerable data inefficiency and heavy reliance on human labor. Spatially, VLMs operate within a high-level semantic space, whereas robotic actions are grounded in low-level 3D physical space; temporally, VLMs primarily interpret the present, while VLA models anticipate future actions. To overcome these challenges, we propose a novel training paradigm, ROSA, which leverages robot state estimation to improve alignment between vision-language and action spaces. By integrating robot state estimation data obtained via an automated process, ROSA enables the VLA model to gain enhanced spatial understanding and self-awareness, thereby boosting performance and generalization. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of ROSA, particularly in low-data regimes.

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