OpenVLA: An Open-Source Vision-Language-Action Model
Authors: name: Moo Jin Kim, name: Karl Pertsch, name: Siddharth Karamcheti, name: Ted Xiao, name: Ashwin Balakrishna, name: Suraj Nair, name: Rafael Rafailov, name: Ethan Foster, name: Grace Lam, name: Pannag Sanketi, name: Quan Vuong, name: Thomas Kollar, name: Benjamin Burchfiel, name: Russ Tedrake, name: Dorsa Sadigh, name: Sergey Levine, name: Percy Liang, name: Chelsea Finn

Large policies pretrained on a combination of Internet-scale vision-language
data and diverse robot demonstrations have the potential to change how we teach
robots new skills: rather than training new behaviors from scratch, we can
fine-tune such vision-language-action (VLA) models to obtain robust,
generalizable policies for visuomotor control. Yet, widespread adoption of VLAs
for robotics has been challenging as 1) existing VLAs are largely closed and
inaccessible to the public, and 2) prior work fails to explore methods for
efficiently fine-tuning VLAs for new tasks, a key component for adoption.
Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source
VLA trained on a diverse collection of 970k real-world robot demonstrations.
OpenVLA builds on a Llama 2 language model combined with a visual encoder that
fuses pretrained features from DINOv2 and SigLIP. As a product of the added
data diversity and new model components, OpenVLA demonstrates strong results
for generalist manipulation, outperforming closed models such as RT-2-X (55B)
by 16.5% in absolute task success rate across 29 tasks and multiple robot
embodiments, with 7x fewer parameters. We further show that we can effectively
fine-tune OpenVLA for new settings, with especially strong generalization
results in multi-task environments involving multiple objects and strong
language grounding abilities, and outperform expressive from-scratch imitation
learning methods such as Diffusion Policy by 20.4%. We also explore compute
efficiency; as a separate contribution, we show that OpenVLA can be fine-tuned
on consumer GPUs via modern low-rank adaptation methods and served efficiently
via quantization without a hit to downstream success rate. Finally, we release
model checkpoints, fine-tuning notebooks, and our PyTorch codebase with
built-in support for training VLAs at scale on Open X-Embodiment datasets.