Galaxea AI logo

Galaxea AI

Robot makerBeijing, ChinaFounded 2023

Builds R1-series mobile bimanual robots and releases the Galaxea Open-World Dataset and the G0 / G0.5 VLA models.

Overview

Galaxea AI is a Beijing-based embodied AI company founded in September 2023 by a team from Tsinghua University and Stanford, led by CEO Gao Jiyang, formerly of Waymo and Momenta. It builds the R1 Pro and R1 Lite wheeled dual-arm robot platforms and develops vision-language-action and world models. In April 2026 it raised RMB 2 billion at a valuation above RMB 20 billion.

Sources: news.pedaily.cn (11 Feb 2026) · finance.yahoo.com (30 Aug 2025) · caixinglobal.com (2 Apr 2026)

Type
Robot maker
Headquarters
Beijing, Chinafinance.yahoo.com, 30 Aug 2025
Founded
2023news.pedaily.cn, 11 Feb 2026

Key people

Funding history

Private company · $535M disclosed in USD across 3 of 5 listed rounds

Funding rounds for Galaxea AI
DateRoundAmountLeadSource
Jan 2024AngelTens of millions of USDIDG Capitalnews.pedaily.cn, 11 Feb 2026
Aug 2024Pre-AOver RMB 200MHillhouse, Ant Groupnews.pedaily.cn, 11 Feb 2026
9 Jul 2025Series A4 and A5Valuation: $700MOver $100M (two rounds combined)Capital Today, Meituan Long-Z, Meituan strategic investmentfinance.yahoo.com, 30 Aug 2025
11 Feb 2026Series BValuation: RMB 10BRMB 1B (about $144M)Jinding Capital (among leads)caixinglobal.com, 12 Feb 2026
2 Apr 2026UndisclosedValuation: Over RMB 20BRMB 2B (about $291M)n/acaixinglobal.com, 2 Apr 2026

Robots and products

Ranking

#7 of 104 model companies · score 81.7 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%96%
  • Open weights 20%100%
  • Adoption 20%61%
  • Released models 15%60%
  • Disclosed funding 10%72%

Each bar shows an input from 0 to 100% before weighting. 3 of 3 listed models have open weights. Most-starred official repository: OpenGalaxea/GalaxeaVLA (816 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 (3)

Galaxea AI logo

G0.5

Open weightsVLA

Autoregressive VLA, initialized from Qwen3.5 2B, in which one transformer decoder generates both reasoning and action tokens; base checkpoint released 2026-06-16, paper 2026-08-12.

Released
16 Jun 2026
Parameters
n/a
License
G0.5 Community License (non-commercial)
Embodiments
mobile manipulator, bimanual, single-arm
GitHub stars
816 as of 19 Oct 2026
HF downloads (all time)
n/a
Published benchmark results for G0.5
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)98.9%arxiv.orgTable 3: Spatial 98.4, Object 100.0, Goal 98.6, Long 98.6. As of 12 Aug 2026
SimplerEnv WidowXAverage success rate on 4 SimplerEnv Bridge (WidowX) tasks (%)87.3%arxiv.orgTable 1: spoon 97.5, carrot 75.0, block 83.3, eggplant 93.3. As of 12 Aug 2026
Galaxea AI logo

G0Plus

Open weightsVLA

Updated G0 VLA checkpoint line (3B), with a 250M G0Tiny variant added on 2026-02-12.

Released
4 Jan 2026
Parameters
3B
License
G0 PLUS Community License (non-commercial)
Embodiments
mobile manipulator, bimanual
GitHub stars
816 as of 19 Oct 2026
HF downloads (all time)
n/a

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

Galaxea AI logo

G0

Open weightsVLA

Dual-system model with a VLM planner and a VLA executor, trained on the Galaxea Open-World Dataset; weights released 2025-09-09.

Released
30 Aug 2025
Parameters
n/a
License
Apache-2.0 (releases before 2026-01-04)
Embodiments
mobile manipulator, bimanual
GitHub stars
816 as of 19 Oct 2026
HF downloads (all time)
n/a

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