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Only First party#Model releases
  1. Beijing Humanoid Robot Innovation Center70

    Beijing Humanoid releases Hi-WBC general whole-body control model on "Huisikaiwu" platform

    Beijing Humanoid released the Hi-WBC general whole-body control model on the "Huisikaiwu" platform on November 2. The model combines human motion references with real physical constraints, enabling robots to adjust how they execute tasks as payload, external forces, and terrain change. It uses a continual skill expansion framework based on MoE and policy distillation, Contact-aware Learning that incorporates contact force prediction, and Perceptive Control that introduces terrain awareness.

    Why it matters: The original text discloses Hi-WBC's technical approach to handling payload, external force, and terrain constraints beyond motion tracking, offering insight into how whole-body control is moving from demonstrations toward real work.

  1. AgiBot75

    AGIBOT Unveils Worken Metrics Framework, AHA3 Hardware Platform, and G2 Max Industrial Robot at 2026 Innovation Day

    At its 2026 Innovation Day, AGIBOT proposed the Worken (effective intelligent working hours) framework, which measures robots' effective output in real environments using three metrics: number of active robots, autonomous working hours, and output efficiency ratio.

    Why it matters: AGIBOT announced its hardware platform, foundation model, operating system, and mass-production model all at once, giving readers a basis to judge its shift from manufacturing scale to measurable output.

  2. 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. 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. Kinetix AI · YouTube67

    Kinetix AI releases KAI World Model, supporting locomotion and manipulation tasks

    Kinetix AI has released the KAI World Model, which is based on parallel video generation and 4D spatial modeling and can simultaneously generate virtual worlds and reconstruct spatial scenes. The model was pre-trained on millions of videos and supports pure locomotion, pure manipulation, and coordinated locomotion-manipulation tasks; it claims generalization across tasks and across robot embodiments. Live demonstrations include a robotic arm autonomously opening a beverage can and a humanoid robot playing real-time table tennis rallies.

    Why it matters: The original shows live demonstrations of the world model on two types of tasks—a robotic arm opening a can and a humanoid playing table tennis—which can be used to understand its approach to generalization across tasks and embodiments.

  1. NVIDIA75

    NVIDIA Cosmos Expands with New Tools for Training, Evaluating and Deploying World Models

    NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints for training, evaluating and deploying world models.

    Why it matters: The original lists Cosmos's new training tools, simulation checkpoints and data blueprints, and gives specific data on users such as Porsche and ANYbotics, which can be used to assess changes in the world model development workflow.

  1. Shanghai AI Laboratory - Embodied Intelligence73

    Shanghai AI Lab launches decision model Intern·Mingjue, outperforming Jev and releasing 0.8B, 2B, and 4B weights

    Shanghai AI Laboratory has launched the decision model Intern·Mingjue (Decision), which outperforms Jev and is available in 0.8B, 2B, and 4B parameter versions. In RTX 4090 tests, the 0.8B and 2B models had an average end-to-end latency of about 33 milliseconds and about 30 decisions per second, while the 4B model was 44.16 milliseconds and Jev was 109.70 milliseconds.

    Why it matters: The original text provides measured latency, benchmark scores, and a mechanism for routing between large and small models, which readers can use to judge the decision model's suitability for interactive scenarios.

  1. PsiBot/灵初智能66

    Lingchu Intelligence releases Psi-R2.5, using strong Pair Data to solve human-machine data alignment challenges

    Lingchu Intelligence has officially released its embodied model Psi-R2.5, which uses a two-tier architecture with a QwenVL3.5-4B high-level planner plus a Wan2.2-IT2V-5B low-level controller, and proposes the strong Pair Data standard. The model reverse-invokes the Psi-W0 world model to generate human demonstration videos backward from real-robot data, enabling mass production of paired data, and supports in-context learning (ICL) for zero-shot generalization to new tasks. It comes with a HIL+RL post-training framework and is claimed to raise task success rates to 99% after a few iterations taking only 1-2 working days, and has already been deployed at customer sites.

    Why it matters: The original article details the model architecture, the strong Pair Data data standard, and the reverse-generation method, letting readers understand how its data approach supports real-robot generalization.

  1. PsiBot/灵初智能73

    Psi Robotics Releases Psi-R2.5 Model: Scaling Human Data for Embodied Intelligence with Strong Pair Data

    Psi Robotics released the Psi-R25 robot manipulation model. Its core upgrades are improved data quality and data attribute compression: it reviewed and extracted high-quality data from 100,000 hours of data, and used strong pair data to align human data with robot data.

    Why it matters: The original article details how strong pair data is generated and the data quality standards, giving readers a referenceable path for aligning human data to robot data.

  1. Figure AI79

    Figure AI releases Helix 2.5, achieving zero-shot full-body autonomy in 30 unfamiliar homes

    Figure AI released Helix 2.5, which completed three long-horizon full-body behaviors—tidying a living room, folding towels, and making a bed—zero-shot in 30 Bay Area homes where no data had ever been collected, without collecting data or fine-tuning in the evaluation environments.

    Why it matters: The original article provides controlled comparison data on Index pretraining for zero-shot generalization and evaluation details, helping readers understand the actual level of humanoid robot pretraining transfer.

  1. Shanghai AI Laboratory - Embodied Intelligence71

    2026 Pujiang Innovation Forum AI-Enabled Scientific Research Special Forum Held; Shanghai AI Lab Releases ShuSheng-S2 and ShuSheng World Model W0

    The 2026 Pujiang Innovation Forum AI-Enabled Scientific Research Special Forum was held on September 13 at the Shanghai Zhangjiang Science Hall, co-hosted by the Fourth Department of the Ministry of Science and Technology and Shanghai AI Lab. During the forum, Shanghai AI Lab launched the ShuSheng Scientific Multimodal Large Model S2 and the ShuSheng Physical World Model W0, opened the full-stack services of the ShuSheng·DuanYan Scientific Discovery Platform, and jointly released 20 major scientific questions with 14 institutions as well as multiple co-construction achievements of the "Climber Program."

    Why it matters: The official report summarizes multiple advances released at the forum, among which the force-touch fusion and robotic arm operation details of the W0 world model may be of reference to readers in the embodied direction.

  1. 小鹏机器人66

    XPeng XOS 6.3.0 OTA Deploys Second-Generation VLA Upgrade, Adds InfiniVLA and X-Foresight World Model

    XPeng XOS 6.3.0 deploys a second-generation VLA upgrade via OTA, restructuring the model architecture around the time dimension: InfiniVLA lets historical visual information continuously participate in current judgments, with the vehicle side selecting 30 seconds of historical context alongside streaming autoregressive inference.

    Why it matters: The article explains concretely the trade-offs in co-designing model architecture and deployment under onboard compute constraints, making it a useful engineering reference for deploying large models on limited hardware.

  1. 小鹏机器人63

    XPeng's second-generation VLA large model gets its first major upgrade, XOS 6.3.0 to debut globally on the G9L

    XPeng held a sharing and experience day themed "TIME," announcing the first major upgrade to its second-generation VLA large model. The new XOS 6.3.0 version will make its global debut on the XPeng G9L, with the core aim of moving AI from 3D spatial understanding to 4D spatiotemporal understanding.

    Why it matters: The original breaks the upgrade into three specific changes: contextual memory, reasoning approach, and world model. Readers can use these to understand how the shift from spatial understanding to spatiotemporal understanding is realized.

  1. Skild AI76

    Skild AI releases robot foundation model S1, achieving in-context learning through video demonstrations

    Skild AI released the robot foundation model S1, which uses video demonstrations as task prompts and can execute manipulation tasks not seen during training without fine-tuning, with long-horizon tasks lasting up to about 10 minutes. At a pretraining data scale of 100,000 hours, the ICL model achieved a 66% success rate on unseen tasks, compared with 9% for the language-prompted VLA; a single demonstration was equivalent to about 380 post-training demonstrations, and the planting task took 11 minutes from recording the demonstration to autonomous execution. S1 was trained on NVIDIA AI infrastructure and has already been put into use with commercial partners.

    Why it matters: The original article provides comparison figures for ICL and VLA at the same data scale and a deployment timeline, allowing readers to assess the feasibility of the in-context learning approach for manipulation tasks.

  1. 北京智在无界科技有限公司64

    BeingBeyond releases Being-H0.8, the first implicit world action model to introduce touch in pretraining

    BeingBeyond has released Being-H0.8, which it calls the world's first implicit tactile world action model, unifying vision, touch, action, and future state changes into a single latent space, with pretraining on more than 500,000 hours of first-person human video.

    Why it matters: The model brings touch into large-scale pretraining along with a self-developed pseudo-labeling system, letting readers understand the path by which tactile data goes from a lab signal to a scalable pretraining resource.

  1. Sunday Robotics78

    Sunday Robotics previews ACT-2 model with 99.1% zero-shot success rate in clothes folding

    Sunday Robotics previewed its robot model ACT-2, which achieved a 99.1% (±0.3%) zero-shot success rate in clothes folding across diverse unseen home environments with zero per-home adaptation, covering 9 clothing categories across 785 autonomous attempts, with an average folding quality of 4.72/5.

    Why it matters: The official disclosure provides detailed quantitative data on pretraining scale and generalization gaps, allowing readers to understand how a small amount of demonstration data transfers to unseen environments.

  1. LimX Dynamics61

    LimX Dynamics releases humanoid VLA model V³-0 alongside COSA 0.5

    LimX Dynamics released V³-0 alongside COSA 0.5, a humanoid VLA technology stack in its VLA-tools layer that supports mobile whole-body manipulation, running on the 43-DOF LimX humanoid robot Oli.

    Why it matters: The original text explains the architectural composition of V³-0 and its human-in-the-loop real-robot reinforcement learning loop, which lets readers understand where the mobile whole-body manipulation capability comes from.

  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. 蚂蚁灵波科技 · YouTube67

    LingBot-VA 2.0 released: a world-action model for physical-world robot control

    Ant Group's Lingbo Technology has released LingBot-VA 2.0, which it describes as a world-action model pretrained from scratch for physical-world robot control, running real-time inference at 150 Hz on a single GPU and able to play air hockey, tidy desks, and pick chips from a conveyor belt.

    Why it matters: It provides a same-benchmark success-rate comparison with π0.5 and single-GPU real-time inference metrics, helping to gauge where this kind of world-action model stands in terms of performance.

  1. 蚂蚁灵波科技 · YouTube70

    Ant Lingbo Open-Sources Embodied Video Foundation Model LingBot-Video, Scoring 0.620 on RBench

    Ant Lingbo has open-sourced LingBot-Video, a MoE video foundation model for embodied intelligence. It uses a DiT plus sparse MoE architecture with 30B parameters and 3B activated at inference, and is claimed to be about 3 times more efficient than dense models.

    Why it matters: The material provides comparison scores on RBench and the scope of the open-source release, helping readers judge the starting point and usability of this embodied video model.