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  1. 机器之心 · 微信公众号78

    Yuance Future Releases Anchor 1 Whole-Body Control Model for Humanoid Robots

    Yuance Future has released Anchor 1, a whole-body control model positioned as the System 0 cerebellum layer within a whole-body intelligence architecture. It uses five anchor points—the pelvis, left and right wrists, and left and right ankles—as commands and does not rely on online retargeting.

    Why it matters: The original article provides quantitative comparisons of the whole-body control model's real-machine error and teleoperation collection efficiency, which can be used to assess how the cerebellum layer's precision affects the success rate of upper-layer VLA tasks.

  1. 机器之心 · 微信公众号80

    LiberAI releases world action model Liber-0, publishes technical report and online simulator

    LiberAI, founded by Liu Songming, has released Liber-0, a new-generation world action model that uses three expert streams for language, vision, and action along with Multimodal Attention to jointly predict future frames and actions.

    Why it matters: The technical report uses controlled experiments to break down the contributions of human data, action supervision, and embodiment diversity to dexterous manipulation, which can inform judgments about data-mix strategies.

  1. 机器之心 · 微信公众号78

    Fysics AI and Fudan propose Fysiverse-UniWAM unified world-action model, ranks first in both navigation and mobile manipulation across 24 real-robot tests

    Fysics AI, together with Fudan University's CITLab, proposed the unified world-action model Fysiverse-UniWAM, which uses a single model to handle autonomous navigation, mobile manipulation, and fixed-workstation dexterous manipulation.

    Why it matters: Readers can see a unified world-action model approach that does not rely on large-scale robot pretraining, along with the specific design of a shared backbone for navigation and manipulation.

  1. Shanghai AI Laboratory - Embodied Intelligence60

    Shanghai AI Laboratory and partners jointly launch materials multimodal large model "Caihua·MatVerse"

    Suzhou National Laboratory, Shanghai AI Laboratory, Shanghai Jiao Tong University and others jointly launched the "Caihua·MatVerse" materials multimodal large model and agent application system, which uses an "independent encoders plus unified decoder" architecture to align XRD spectra, electron microscope images and molecular structures into the same semantic space.

    Why it matters: The materials present a multimodal unified representation architecture and 19-category disciplinary evaluation benchmarks, offering insight into how materials large models can move from retrieval to mechanistic reasoning.

  1. 机器人前瞻 · 微信公众号82

    NVIDIA open-sources robot data engine Flora, labeling cost about 1/125 that of GPT-6 Astra

    NVIDIA released and open-sourced the data engine Flora, which converts raw robot video into training data such as timestamped action descriptions, process rewards, and outcome rewards; it is already used to train the GR00T series models. Flora consists of an action description model, a temporal segmentation model, and a reward model, fine-tuned from the Qwen3.5 series of vision-language models, and can label 200,000 hours of data per day on 512 GB300 GPUs.

    Why it matters: NVIDIA's open-source data engine cuts robot video labeling costs to about 1/125 of a general-purpose large model and provides several benchmark comparisons, which can be used to assess the substitution space in the data labeling segment.

  1. Xbotics具身智能实验室 · 微信公众号76

    SenseTime launches SenseNova-RoboRSI and a 27B embodied model, achieving SOTA on 12 simulation benchmarks

    The SenseTime SenseNova team launched the SenseNova-RoboRSI unified execution framework and the 27B embodied model SenseNova-RoboRSI-27B-Preview.

    Why it matters: Through comparison data from the same model with different execution systems, readers can intuitively understand the magnitude of the execution framework's impact on embodied task performance and its cost.

  2. 智源研究院 · 微信公众号68

    BAAI's FlagOS 2.2 released: over 1,454 operators, 27% cross-chip performance gain

    BAAI presented FlagOS 2.2 progress at the 2026 AI Open Computing Conference and FlagOS Technical Conference; the version was released on September 30. Public operators in the nine operator libraries rose from 564 to 1,454 (890 added). In tests covering over a thousand operators and 10 heterogeneous AI chips, cross-chip operator performance improved by about 27% over the previous version. The FlagOS-Compressor weight conversion and quantization tool was released for the first time, enabling a new model to be adapted to as many as 12 chips within one day.

    Why it matters: The original text provides specific figures for operators, quantization, and serving throughput, which readers can use to assess the performance and cost potential of existing chips for running new models.

  1. RoboParty · 微信公众号67

    RoboParty Lab, Zhejiang University, and Tsinghua AIR release and open-source world model control method INTACT

    RoboParty Lab, Zhejiang University, Tsinghua University Institute for AI Industry Research (AIR), and InSpatio jointly released and open-sourced the world model control method INTACT (INtent-To-ACTion).

    Why it matters: The original article provides latency and success-rate comparisons with traditional CEM search, which readers can use to judge the benefit boundaries of search-free world model control.

  1. 正行创新 · X68

    Harness VLA open-sourced, achieves 82.4% on LIBERO-Pro

    @ChaoYuTHU has open-sourced Harness VLA, which adds a Harness Layer to organize VLA models without modifying their weights. On LIBERO-Pro, Harness VLA scores 82.4%, compared with Pi_RLinf at 50%, Berkeley RATS at 43.8%, and NVIDIA Cap-X at 18.2%. Striding AI posted this tweet and sees it as a possible sign of a shift from end-to-end VLA to Harness VLA.

    Why it matters: The tweet provides comparison numbers with other VLA approaches on the same benchmark, letting readers judge how much headroom a plug-in structure that does not change weights offers.

  1. PsiBot/灵初智能81

    Lingchu Intelligence and PKU-Lingchu Joint Lab Release and Open-Source Dual Dexterous Hand General Manipulation Model and Full-Stack System EgoSteer

    Lingchu Intelligence and the PKU-Lingchu Joint Lab released and open-sourced the dual dexterous hand general manipulation model and full-stack system EgoSteer, comprising the data processing pipeline EgoSmith, the robotics technology stack Robot-Stack, and a world-model-enhanced VLA model.

    Why it matters: The original text presents a complete chain from human-hand data processing and real-robot correction to model architecture and experimental figures, allowing readers to assess its reproducibility value.

  1. 智源研究院 · 微信公众号74

    BAAI releases multimodal representation world model Wujie·RoboBrain Orca

    BAAI has released the multimodal representation world model Wujie·RoboBrain Orca. Its core idea is to shift from predicting the next word, next frame, or next action to predicting the next world state: it first learns a unified latent world representation, then adapts to downstream tasks through three lightweight readout modules for text, images, and actions.

    Why it matters: The original text explains the training paradigm shift from predicting the next output to predicting the next state, as well as the approach of a frozen backbone plus lightweight readout modules, which is transferable to readers following the world model route.

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