OpenVLA team

Research labUnited States

Academic collaboration led from Stanford that released OpenVLA, an open-weights 7B vision-language-action model, and the OFT fine-tuning recipe for it.

Overview

OpenVLA is a 7-billion-parameter open-source vision-language-action model trained on 970k real-world robot demonstrations, released in June 2024 (arXiv 2406.09246). It is built on a Llama 2 language model with a visual encoder combining DINOv2 and SigLIP features, and the authors released checkpoints, fine-tuning notebooks and a PyTorch codebase. The 18 authors are affiliated with Stanford University, UC Berkeley, Toyota Research Institute, Google DeepMind, Physical Intelligence and MIT.

Sources: arxiv.org (13 Jun 2024) · openvla.github.io (17 Oct 2026)

Type
Research lab

Key people

  • Moo Jin KimCo-lead author (equal contribution), OpenVLA; Stanford Universityopenvla.github.io, 17 Oct 2026
  • Karl PertschCo-lead author (equal contribution), OpenVLA; Stanford University and UC Berkeleyopenvla.github.io, 17 Oct 2026
  • Siddharth KaramchetiCo-lead author (equal contribution), OpenVLA; Stanford University and Toyota Research Instituteopenvla.github.io, 17 Oct 2026
  • Chelsea FinnSenior author, OpenVLA; Stanford Universityarxiv.org, 13 Jun 2024
  • Percy LiangSenior author, OpenVLA; Stanford Universityarxiv.org, 13 Jun 2024
  • Sergey LevineSenior author, OpenVLA; UC Berkeleyarxiv.org, 13 Jun 2024

Funding history

Research lab · Academic research collaboration; no separate funding

Ranking

#3 of 104 model companies · score 88.2 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%98%
  • Open weights 20%100%
  • Adoption 20%80%
  • Released models 15%60%
  • Disclosed funding 10%n/a

Each bar shows an input from 0 to 100% before weighting. 3 of 3 listed models have open weights. Most-starred official repository: openvla/openvla (7.2k stars, 19 Oct 2026).

How we rank model companies

The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.

InputWeightHow it is measured
Published benchmarks35%For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating).
Open weights20%Share of the company's listed models whose weights the public can download.
Adoption20%GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero.
Released models15%Number of publicly announced, named models, capped at 5.
Disclosed funding10%The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion.

Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.

Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.

Submit or correct data →

Open-weights models (3)

OpenVLA-OFT

Open weightsVLA

OpenVLA fine-tuned with the Optimized Fine-Tuning recipe (parallel decoding, action chunking, continuous actions, L1 regression), evaluated on LIBERO and on a bimanual ALOHA robot.

Released
27 Feb 2025
License
MIT
Embodiments
single-arm, bimanual
GitHub stars
1.4k as of 19 Oct 2026
HF downloads (all time)
51k as of 19 Oct 2026
Published benchmark results for OpenVLA-OFT
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)97.1%arxiv.orgTable I, OpenVLA-OFT with third-person + wrist images, proprio and language (Spatial 97.6 / Object 98.4 / Goal 97.9 / Long 94.5); third-person-image-only variant = 95.3. As of 27 Feb 2025

MiniVLA

Open weightsVLA

Smaller OpenVLA variant from Stanford ILIAD (Belkhale and Sadigh) that replaces the Llama 2 7B backbone with Qwen 2.5 0.5B and adds vector-quantized action chunking.

Released
12 Dec 2024
Parameters
~1B
License
n/a
Embodiments
single-arm
GitHub stars
381 as of 19 Oct 2026
HF downloads (all time)
n/a

No benchmark results on a shared public benchmark have been published for this model.

OpenVLA

Open weightsVLA

Open-weights vision-language-action model built on a Llama 2 backbone with DINOv2 and SigLIP vision encoders, trained on 970k Open X-Embodiment robot episodes.

Released
13 Jun 2024
Parameters
7B
License
MIT (code); weights may inherit Llama 2 Community License restrictions
Embodiments
single-arm
GitHub stars
7.2k as of 19 Oct 2026
HF downloads (all time)
18.1M as of 19 Oct 2026
Published benchmark results for OpenVLA
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)76.5%arxiv.orgAppendix E.2, Table 12, fine-tuned OpenVLA (Spatial 84.7 / Object 88.4 / Goal 79.2 / Long 53.7), mean of 3 seeds. As of 13 Jun 2024