Research labBeijing, ChinaFounded 2018
Non-profit AI research institute that releases the open RoboBrain embodied-reasoning models and the RoboBrain-X0 cross-embodiment action model.
The Beijing Academy of Artificial Intelligence (BAAI, 智源研究院) is a new-type R&D institution in Beijing's AI sector, founded in November 2018. Its embodied-AI work includes the open-source RoboBrain models; RoboBrain 2.0 was announced on 2025-06-06 with 3B, 7B and 32B checkpoints released between June and July 2025. Wang Zhongyuan became president in February 2024, succeeding founding president Huang Tiejun, who remains chairman.
Sources: hub.baai.ac.cn (2 Feb 2024) · github.com (17 Oct 2026)
Research lab · Non-profit new-type research institution in Beijing; no disclosed venture funding
#32 of 104 model companies · score 46.4 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 3 of 3 listed models have open weights. Most-starred official repository: FlagOpen/RoboBrain2.5 (1.2k stars, 19 Oct 2026).
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.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | 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 weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | 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 models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | 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.
Open weightsEmbodied reasoning
Update to RoboBrain adding depth-aware 3D trace prediction and dense temporal progress estimation; 8B released in NVIDIA- and Moore Threads-trained variants.
No benchmark results on a shared public benchmark have been published for this model.
Open weightsVLA
Cross-embodiment action model based on RoboBrain 2.0 that maps different robots into a shared discrete action vocabulary via grouped residual quantization.
No benchmark results on a shared public benchmark have been published for this model.
Open weightsEmbodied reasoning
Embodied vision-language model for spatial understanding, temporal reasoning and planning, released in 7B and 32B sizes.
No benchmark results on a shared public benchmark have been published for this model.
