Unified Vision-Language-Action Model
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.
685b63c2d2ee4fac76521dee
Yuqi Wang
685b63c2d2ee4fac76521def
Xinghang Li
685b63c2d2ee4fac76521df0
Wenxuan Wang
685b63c2d2ee4fac76521df1
Junbo Zhang
685b63c2d2ee4fac76521df2
Yingyan Li
685b63c2d2ee4fac76521df3
Yuntao Chen
685b63c2d2ee4fac76521df4
Xinlong Wang
685b63c2d2ee4fac76521df5
Zhaoxiang Zhang
Unified Vision-Language-Action Model
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.
UniVLA is a multimodal VLA model that autoregressively processes vision, language, and action as token sequences, incorporating world modeling for effective long-horizon policy learning and achieving state-of-the-art results across simulation and real-world benchmarks.
vision-language-action models VLAs vision-language models VLMs autoregressive models discrete token sequences multimodal tasks learning world modeling causal dynamics policy learning simulation benchmarks CALVIN LIBERO Simplenv-Bridge ALOHA manipulation autonomous driving