Research labLos Altos, CA, United StatesFounded 2015
Toyota's research institute; its robotics group develops diffusion-based visuomotor policies, including Diffusion Policy (with Columbia and MIT) and Large Behavior Models for dexterous bimanual manipulation.
Toyota Research Institute (TRI) is a research and development company of Toyota, announced on November 6, 2015 with an initial investment of $1 billion over five years and operating since January 2016. It is headquartered in Los Altos, California, with a robotics lab in Cambridge, Massachusetts, and is led by CEO Gill Pratt. Its robotics work includes Large Behavior Models; in August 2025 TRI and Boston Dynamics showed a single Large Behavior Model controlling the whole body of the Atlas humanoid.
Sources: global.toyota (6 Nov 2015) · tri.global (17 Oct 2026) · tri.global (20 Aug 2025)
Corporate division · Subsidiary of Toyota Motor Corporation, launched with a $1 billion five-year commitment from Toyota; no external funding rounds disclosed.
#52 of 104 model companies · score 34.7 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 1 of 2 listed models have open weights. Most-starred official repository: real-stanford/diffusion_policy (4.7k 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
Visuomotor policy that represents robot actions as a conditional denoising diffusion process; joint work of Columbia University, Toyota Research Institute and MIT.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsPolicy
Multitask diffusion-transformer policy pretrained on teleoperated bimanual manipulation data, studied in the paper 'A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation' and evaluated in simulation (lbm_eval on Drake) and on hardware.
No benchmark results on a shared public benchmark have been published for this model.
