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
claimed_verified
2025-06-30T06:23:10.589Z
649fe21d59c1ae90dbfacf91
/avatars/7f77fa77113e80cb45406927e2386387.svg
Wang Yuqi
user
Yuqi1997
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