Big techChina
Embodied-AI company under Ant Group that releases open VLA, video-action world models and perception models under the LingBot name.
Robbyant is a company under Ant Group focused on building a foundational platform for embodied AI. In January 2026 it open-sourced LingBot-VLA, a vision-language-action model trained on about 20,000 hours of real-world dual-arm robot data, together with the LingBot-VA video-action world model. It released LingBot-VLA 2.0 in July 2026, reporting 60,000 hours of training data across 20 robot morphologies from 17 manufacturers.
Sources: huggingface.co (19 Oct 2026) · arxiv.org (26 Jan 2026) · itbrief.co.uk (8 Jul 2026)
Corporate division · Embodied-AI company under Ant Group (per its Hugging Face organisation page); no separate funding round disclosed in sources reviewed.
#30 of 104 model companies · score 52.5 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 4 of 4 listed models have open weights. Most-starred official repository: Robbyant/lingbot-world (4.5k 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 weightsVLA
Second version pretrained on 60,000 hours of robot and human manipulation data across 20 robot types, adding head, waist, hand and chassis control.
No benchmark results on a shared public benchmark have been published for this model.
Open weightsWorld model
Open world simulator derived from video generation (built on Wan2.2) with camera-pose control and minute-level horizons, aimed at content creation, gaming and robot learning.
No benchmark results on a shared public benchmark have been published for this model.
Open weightsWorld model
Autoregressive diffusion video-action world model that learns frame prediction and policy execution jointly for robot control.
No benchmark results on a shared public benchmark have been published for this model.
Open weightsVLA
Vision-language-action model pre-trained on about 20,000 hours of real-world data from 9 dual-arm robot configurations, released with and without a depth variant.
No benchmark results on a shared public benchmark have been published for this model.
