Unified Vision-Language-Action Model

• URL: http://arxiv.org/abs/2506.19850v1
• Date: Tue, 24 Jun 2025 17:59:57 GMT
• Title: Unified Vision-Language-Action Model
• Authors: Yuqi Wang, Xinghang Li, Wenxuan Wang, Junbo Zhang, Yingyan Li, Yuntao Chen, Xinlong Wang, Zhaoxiang Zhang,
• Abstract summary: We present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences.<n>Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge.<n>We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.
• Score: 86.68814779303429
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models (VLMs) to generate action signals, often overlooking the rich temporal and causal structure embedded in visual observations. In this paper, we present UniVLA, a unified and native multimodal VLA model that autoregressively models vision, language, and action signals as discrete token sequences. This formulation enables flexible multimodal tasks learning, particularly from large-scale video data. By incorporating world modeling during post-training, UniVLA captures causal dynamics from videos, facilitating effective transfer to downstream policy learning--especially for long-horizon tasks. Our approach sets new state-of-the-art results across several widely used simulation benchmarks, including CALVIN, LIBERO, and Simplenv-Bridge, significantly surpassing previous methods. For example, UniVLA achieves 95.5% average success rate on LIBERO benchmark, surpassing pi0-FAST's 85.5%. We further demonstrate its broad applicability on real-world ALOHA manipulation and autonomous driving.

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