OpenDriveLab (HKU) logo

OpenDriveLab (HKU)

Research labHong Kong, China

Embodied AI and autonomous driving lab at the University of Hong Kong; released the UniVLA latent-action policy.

Overview

OpenDriveLab is an embodied AI and autonomous driving research group at the University of Hong Kong led by Hongyang Li and Ping Luo. In May 2025, with AgiBot co-authors, it released UniVLA, a generalist policy that learns task-centric latent actions from videos across embodiments and decodes them into robot actions. Weights for pre-trained and fine-tuned UniVLA checkpoints are on Hugging Face under Apache-2.0.

Sources: opendrivelab.com (19 Oct 2026) · arxiv.org (9 May 2025) · huggingface.co (19 Oct 2026)

Type
Research lab
Headquarters
Hong Kong, Chinaopendrivelab.com, 19 Oct 2026

Key people

  • Qingwen BuLead author, UniVLAarxiv.org, 9 May 2025
  • Ping LuoSenior author, UniVLAarxiv.org, 9 May 2025
  • Hongyang LiSenior author, UniVLAarxiv.org, 9 May 2025

Funding history

Research lab · University research lab at the University of Hong Kong; no separate funding.

Ranking

#21 of 104 model companies · score 68.3 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%74%
  • Open weights 20%100%
  • Adoption 20%64%
  • Released models 15%20%
  • Disclosed funding 10%n/a

Each bar shows an input from 0 to 100% before weighting. 1 of 1 listed models have open weights. Most-starred official repository: OpenDriveLab/UniVLA (1.1k 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 (1)

OpenDriveLab (HKU) logo

UniVLA

Open weightsVLA

Generalist policy that plans in a task-centric latent action space learned from cross-embodiment robot data and human video, then decodes latent actions into embodiment-specific controls.

Released
9 May 2025
Parameters
7B
License
Apache-2.0
Embodiments
single-arm, mobile robot
GitHub stars
1.1k as of 19 Oct 2026
HF downloads (all time)
9.9k as of 19 Oct 2026
Published benchmark results for UniVLA
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)95.2%arxiv.orgTable I / Sec. IV-A1: average over four suites, pre-trained on full data mix. As of 3 Nov 2025
CALVIN ABC→DCALVIN ABC->D average successful length (0-5)3.80arxiv.orgTable A-II, third-view RGB only. As of 3 Nov 2025
SimplerEnv WidowXSimplerEnv WidowX (Bridge) average task success (%)42.7%arxiv.orgAppendix B2 / Table A-III, full fine-tuning. As of 3 Nov 2025