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Only Models#AgiBot
  1. 机器人前瞻 · 微信公众号78

    AgiBot releases WITA-OMNI interaction model and Lingqu OS 1.0, full open source by year-end

    At its 2026 Innovation Day, AgiBot released WITA-OMNI, an embodied omni-modal end-to-end interaction large model. It ranked first on four benchmarks: Daily-Omni, TimeLens-Bench, EmbSpatialBench, and MMAU, with an average response time of about 600 milliseconds.

    Why it matters: AgiBot released an interaction model, a native operating system, and an ecosystem plan all at once, giving readers a way to understand its path to opening up its technology foundation and how it accumulates real-world interaction data.

  1. 36Kr Embodied Intelligence78

    AgiBot Releases GO-3 Preview, WITA-OMNI and AGILE, Connecting the Full Physical AI Chain

    At its Innovation Day, AgiBot released the GO-3 Preview embodied foundation model, the WITA-OMNI native multimodal interaction model and the AGILE general motion intelligence algorithm framework, and also launched Lingqu OS 1.0 and the AHA3 third-generation hardware architecture.

    Why it matters: AgiBot released three types of models—task execution, interaction and motion—along with software and hardware architectures in one go, giving readers a clear view of its deployment approach across the full physical AI chain.

  2. 新智元 · 微信公众号78

    AgiBot releases GO-3 Preview embodied model, tops three robotics leaderboards and plans full open source by year-end

    AgiBot officially released its GO-3 Preview embodied model on October 29, taking Top 1 on robotics leaderboards including RoboDojo, RoboTwin, and RoboColiseum, and expects to fully open source it by year-end.

    Why it matters: AgiBot links pretraining, post-training, and post-deployment learning into a single pipeline, which readers can use to compare differences in scaling approaches for embodied models.

  3. 机器之心 · 微信公众号78

    AgiBot releases embodied model GO-3 Preview, enabling full-pipeline scaled learning from pretraining to post-deployment

    AgiBot has released the embodied model GO-3 Preview, focused on full-pipeline scaled learning across pretraining, post-training, and post-deployment, and it ranked first in three evaluations: RoboDojo, RoboTwin, and RoboColiseum.

    Why it matters: AgiBot disclosed GO-3 Preview's three-stage learning pipeline of pretraining, post-training, and post-deployment, along with specific benefit data from mixed training using real-machine clusters and world models.

  4. AgiBot72

    AgiBot releases embodied foundation model GO-3 Preview, ranks first on three embodied intelligence benchmarks

    AgiBot has released the embodied foundation model GO-3 Preview, built on its self-developed WRAM (World Reasoning Action Model) architecture that unifies task understanding, future state prediction, and action generation, and it ranks first on three embodied intelligence benchmarks: RoboDojo, RoboTwin, and RoboColiseum. It is planned to be fully open-sourced by the end of 2026.

    Why it matters: The original article fully discloses GO-3 Preview's three-stage design of pretraining, post-training, and post-deployment learning, which can be used to understand the engineering path for scaling embodied models across their full lifecycle.

  1. arXiv cs.RO77

    AgiBot Team Releases GE-Act 2.0 World-Action Model Technical Report

    The AgiBot Research Team released a technical report on GE-Act 2.0, a world-action model whose generative and action components are all trained from scratch on manipulation data. It consists of a control-oriented autoencoder (CoAE), a single-step visual planner (SVP), and an inverse dynamics model (IDM), and is jointly trained with KASO.

    Why it matters: The original text presents the component design and data-scaling curves for pretraining a world-action model from scratch, allowing observation of changes in zero-shot cross-embodiment transfer.

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