VLA-0: Building State-of-the-Art VLAs with Zero Modification
Vision-Language-Action models (VLAs) hold immense promise for enabling
generalist robot manipulation. However, the best way to build them remains an
open question. Current approaches often add complexity, such as modifying the
existing vocabulary of a Vision-Language Model (VLM) with action tokens or
introducing special action heads. Curiously, the simplest strategy of
representing actions directly as text has remained largely unexplored. This
work introduces VLA-0 to investigate this idea. We find that VLA-0 is not only
effective; it is surprisingly powerful. With the right design, VLA-0
outperforms more involved models. On LIBERO, a popular benchmark for evaluating
VLAs, VLA-0 outperforms all existing methods trained on the same robotic data,
including pi_0.5-KI, OpenVLA-OFT and SmolVLA. Furthermore, without
large-scale robotics-specific training, it outperforms methods trained on
large-scale robotic data, like pi_0.5-KI, pi_0, GR00T-N1 and MolmoAct.
These findings also translate to the real world, where VLA-0 outperforms
SmolVLA, a VLA model pre-trained on large-scale real data. This paper
summarizes our unexpected findings and spells out the specific techniques
required to unlock the high performance of this simple yet potent VLA design.
Visual results, code, and trained models are provided here:
https://vla0.github.io/.
68f0817e36f8b025381e1a20
Ankit Goyal
68f0817e36f8b025381e1a21
Hugo Hadfield
68f0817e36f8b025381e1a22
Xuning Yang
68f0817e36f8b025381e1a23
Valts Blukis
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Fabio Ramos
VLA-0: Building State-of-the-Art VLAs with Zero Modification
Vision-Language-Action models (VLAs) hold immense promise for enabling
generalist robot manipulation. However, the best way to build them remains an
open question. Current approaches often add complexity, such as modifying the
existing vocabulary of a Vision-Language Model (VLM) with action tokens or
introducing special action heads. Curiously, the simplest strategy of
representing actions directly as text has remained largely unexplored. This
work introduces VLA-0 to investigate this idea. We find that VLA-0 is not only
effective; it is surprisingly powerful. With the right design, VLA-0
outperforms more involved models. On LIBERO, a popular benchmark for evaluating
VLAs, VLA-0 outperforms all existing methods trained on the same robotic data,
including pi_0.5-KI, OpenVLA-OFT and SmolVLA. Furthermore, without
large-scale robotics-specific training, it outperforms methods trained on
large-scale robotic data, like pi_0.5-KI, pi_0, GR00T-N1 and MolmoAct.
These findings also translate to the real world, where VLA-0 outperforms
SmolVLA, a VLA model pre-trained on large-scale real data. This paper
summarizes our unexpected findings and spells out the specific techniques
required to unlock the high performance of this simple yet potent VLA design.
Visual results, code, and trained models are provided here:
https://vla0.github.io/.
A simple VLA model, VLA-0, outperforms more complex models on robotic manipulation tasks by representing actions as text without additional modifications or large-scale training.
Vision-Language-Action models Vision-Language Model action tokens action heads LIBERO $\pi_0.5$-KI OpenVLA-OFT SmolVLA GR00T-N1 MolmoAct