Unified Vision-Language-Action Model
Authors: name: Yuqi Wang, name: Xinghang Li, name: Wenxuan Wang, name: Junbo Zhang, name: Yingyan Li, name: Yuntao Chen, name: Xinlong Wang, name: Zhaoxiang Zhang

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.