Shanghai AI Laboratory logo

Shanghai AI Laboratory

Research labShanghai, ChinaFounded 2020

State-backed AI research lab whose InternRobotics group releases the InternVLA manipulation and navigation models and InternData datasets.

Overview

Shanghai Artificial Intelligence Laboratory (上海人工智能实验室) is a state-backed AI research institution in Shanghai, unveiled on 2020-07-11 at the World Artificial Intelligence Conference. Its embodied-AI work is published under the Intern Robotics (InternRobotics) open-source organization, which includes the InternVLA vision-language-action models and navigation, data and simulation tools. Zhou Bowen has been director and chief scientist since 2024.

Sources: news.sciencenet.cn (12 Jul 2020) · merics.org (13 Dec 2024) · github.com (17 Oct 2026)

Type
Research lab
Headquarters
Shanghai, Chinanews.sciencenet.cn, 12 Jul 2020
Founded
2020news.sciencenet.cn, 12 Jul 2020

Key people

Funding history

Research lab · State-backed new-type research institution; no disclosed venture funding

Ranking

#5 of 104 model companies · score 86.3 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%94%
  • Open weights 20%100%
  • Adoption 20%64%
  • Released models 15%80%
  • Disclosed funding 10%n/a

Each bar shows an input from 0 to 100% before weighting. 4 of 4 listed models have open weights. Most-starred official repository: InternRobotics/InternNav (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 (4)

Shanghai AI Laboratory logo

InternVLA-A1.5

Open weightsVLA

VLA on a Qwen3.5-2B backbone with a flow-matching action expert and learnable latent-foresight tokens supervised by a frozen video generator.

Released
6 Jul 2026
Parameters
3B
License
CC-BY-NC-SA-4.0
Embodiments
single-arm, bimanual
GitHub stars
567 as of 19 Oct 2026
HF downloads (all time)
598 as of 19 Oct 2026
Published benchmark results for InternVLA-A1.5
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)98.9%arxiv.orgTable 5 (Spatial 98.6 / Object 99.8 / Goal 98.6 / Long 98.4). As of 6 Jul 2026
SimplerEnv WidowXWidowX (Bridge) average success rate (%)80.8%arxiv.orgTable 2; Google Robot VM/VA not reported. As of 6 Jul 2026
Shanghai AI Laboratory logo

InternVLA-A1

Open weightsVLA

Mixture-of-Transformers VLA with understanding, visual-foresight generation and action experts, pretrained on InternData-A1 synthetic data plus real robot data.

Released
5 Jan 2026
License
CC-BY-NC-SA-4.0
Embodiments
bimanual
GitHub stars
567 as of 19 Oct 2026
HF downloads (all time)
1.5k as of 19 Oct 2026

No benchmark results on a shared public benchmark have been published for this model.

Shanghai AI Laboratory logo

InternVLA-M1

Open weightsVLA

Spatially guided VLA built on Qwen2.5-VL-3B, with spatial-grounding pretraining followed by spatially prompted action post-training.

Released
15 Oct 2025
Parameters
~4.1B
License
CC-BY-NC-SA-4.0
Embodiments
single-arm
GitHub stars
435 as of 19 Oct 2026
HF downloads (all time)
7.2k as of 19 Oct 2026
Published benchmark results for InternVLA-M1
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)95.9%arxiv.orgTable 4 (Spatial 98.0 / Object 99.0 / Goal 93.8 / Long 92.6). As of 15 Oct 2025
SimplerEnv Google VMGoogle Robot visual matching average success rate (%)80.7%arxiv.orgTable 1. As of 15 Oct 2025
SimplerEnv Google VAGoogle Robot variant aggregation average success rate (%)76%arxiv.orgTable 1. As of 15 Oct 2025
SimplerEnv WidowXWidowX (Bridge) average success rate (%)71.7%arxiv.orgTable 2. As of 15 Oct 2025
Shanghai AI Laboratory logo

InternVLA-N1

Open weightsVLA

Dual-system vision-and-language navigation model (Qwen2.5-VL-based System 2 for pixel-goal grounding plus a fast System 1 policy), released through the InternNav toolbox.

Released
Oct 2025
Parameters
n/a
License
CC-BY-NC-SA-4.0 (weights); InternNav code MIT
Embodiments
quadruped, mobile robot
GitHub stars
1.1k as of 19 Oct 2026
HF downloads (all time)
4k as of 19 Oct 2026

No benchmark results on a shared public benchmark have been published for this model.