Research labStanford, CA, United States
Shuran Song's Robotics and Embodied AI Lab (previously at Columbia); released Diffusion Policy and the UMI policy and data-collection system.
REAL is Shuran Song's Robotics and Embodied Artificial Intelligence Lab at Stanford University; the group was previously based at Columbia University, where Diffusion Policy was first published in March 2023. Diffusion Policy represents a visuomotor policy as a conditional denoising diffusion process over action sequences. In February 2024, with Toyota Research Institute co-authors, the lab released UMI (Universal Manipulation Interface), a hand-held gripper data-collection framework with a released diffusion-policy checkpoint.
Sources: real.stanford.edu (19 Oct 2026) · arxiv.org (7 Mar 2023) · arxiv.org (15 Feb 2024)
Research lab · University research lab (Stanford; formerly Columbia); no separate funding.
#41 of 104 model companies · score 40.4 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 1 of 1 listed models have open weights. Most-starred official repository: real-stanford/universal_manipulation_interface (1.6k 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 weightsPolicy
Hand-held gripper data-collection and policy-learning framework whose diffusion policies transfer from in-the-wild human demonstrations to robots; an in-the-wild cup-arrangement checkpoint is released.
No benchmark results on a shared public benchmark have been published for this model.
