AgiBot (Zhiyuan Robotics) logo

AgiBot (Zhiyuan Robotics)

Robot makerShanghai, ChinaFounded 2023

Builds humanoid and wheeled robots and trains the Genie Operator (GO) VLA models and the Genie Envisioner video world model.

Overview

AgiBot (Zhiyuan Robotics) is a Shanghai-based robotics company founded in February 2023 by former Huawei executive Deng Taihua and former Huawei engineer Peng Zhihui. It reported revenue of RMB 1.05 billion for 2025. In 2025 it acquired a 63.62% controlling stake in STAR Market-listed Sino Composite (Swancor Advanced Materials) for about RMB 2.1 billion. It confirmed the start of a Hong Kong listing process on July 24, 2026, and in October 2026 was reported to be close to a new round at a valuation of about RMB 50 billion.

Sources: eu.36kr.com (27 Sep 2026) · eu.36kr.com (14 Oct 2026) · technode.com (14 Oct 2026) · yicaiglobal.com (4 Aug 2025)

Type
Robot maker
Headquarters
Shanghai, Chinaeu.36kr.com, 14 Oct 2026
Founded
2023eu.36kr.com, 27 Sep 2026
Website
agibot.com

Key people

  • Deng TaihuaFounder, Chairman and CEOeu.36kr.com, 27 Sep 2026
  • Peng ZhihuiCo-founder, President and CTOeu.36kr.com, 27 Sep 2026

Funding history

Private company

Funding rounds for AgiBot (Zhiyuan Robotics)
DateRoundAmountLeadSource
1 Aug 2025StrategicUndisclosedn/afinance.yahoo.com, 1 Aug 2025

Robots and products

Ranking

#19 of 104 model companies · score 71.7 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%99%
  • Open weights 20%67%
  • Adoption 20%73%
  • Released models 15%60%
  • Disclosed funding 10%0%

Each bar shows an input from 0 to 100% before weighting. 2 of 3 listed models have open weights. Most-starred official repository: OpenDriveLab/AgiBot-World (3.2k 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 (2)

AgiBot (Zhiyuan Robotics) logo

Genie Envisioner (GE)

Open weightsWorld model

Instruction-conditioned video diffusion world model (GE-Base) with an action decoder (GE-Act) and an action-conditioned neural simulator (GE-Sim); GE-Base weights released 2025-08-14.

Released
7 Aug 2025
Parameters
n/a
License
CC BY-NC-SA 4.0 (repo; some adapted code Apache-2.0)
Embodiments
bimanual
GitHub stars
590 as of 19 Oct 2026
HF downloads (all time)
74 as of 19 Oct 2026

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

AgiBot (Zhiyuan Robotics) logo

GO-1 (Genie Operator-1)

Open weightsVLA

Generalist policy with a latent-action planner, trained on the AgiBot World dataset; weights (GO-1 and GO-1 Air) were open-sourced on 2025-09-19.

Released
9 Mar 2025
Parameters
3B
License
CC BY-NC-SA 4.0
Embodiments
bimanual, humanoid
GitHub stars
3.2k 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.

Closed or unreleased-weights models (1)

AgiBot (Zhiyuan Robotics) logo

GO-2 (Genie Operator-2)

Closed weightsVLA

Successor to GO-1 that generates an action chain-of-thought plan with a low-frequency planner and a high-frequency action follower in an asynchronous dual-system design.

Released
9 Apr 2026
Parameters
n/a
License
Proprietary
Embodiments
bimanual, humanoid
GitHub stars
n/a
HF downloads (all time)
n/a
Published benchmark results for GO-2 (Genie Operator-2)
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)98.5%agibot.comCompany announcement of GO-2 (no paper table); same figure appears in the AgiBot co-authored ACoT-VLA paper (arXiv 2601.11404), whose link to GO-2 is not stated by AgiBot. As of 9 Apr 2026