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Only Models#Evaluations and benchmarks
  1. 芯东西78

    Ant Group's Lingbo open-sources LingBot-Touch tactile pretrained model, unifying signals from multiple sensor types

    Ant Group's Lingbo on November 10 open-sourced LingBot-Touch, a tactile pretrained foundation model for dexterous manipulation that can encode different sensing signals such as piezoresistive and vision-based tactile signals into unified tactile tokens for use by different dexterous hands and policy models.

    Why it matters: The original article provides comparative data on the cross-sensor tactile pretrained model across 26 real-robot tasks, which can be used to assess the actual impact of tactile representations on policy success rates.

  2. The Robot Report88

    NVIDIA launches GR00T 2 world action model and Isaac Lab 3.0 at CoRL

    NVIDIA launched the open world action model GR00T 2 and the open-source robot learning framework Isaac Lab 3.0 at CoRL. GR00T 2 was trained on 100,000 hours of NVIDIA open-source first-person data; with only one demonstration per task, it reached an average success rate of 71% across 50 tasks, rising to 95% with five demonstrations, and without task-specific demonstrations it completed at least once each of 40 of the 50 tasks.

    Why it matters: GR00T 2's few-shot success rates and Isaac Lab 3.0's training throughput gains provide the current baseline for embodied models and the simulation training stack.

  1. 机器之心 · 微信公众号84

    Qiyuan releases PrimeSim world-model simulator, tops EWMBench and WorldSimProbe

    The Qiyuan Embodied Research Center has released PrimeSim, a world-model simulator for robot manipulation tasks, built by adapting a pretrained video generation model through mid-training and post-training stages. It ranks first on EWMBench with an overall score of 6.09, and took first place in three of five WorldSimProbe evaluations: global trajectory coverage, action-source behavior preservation, and interaction dynamics.

    Why it matters: The original text provides the two-stage training recipe and results across multiple leaderboards, which readers can use to judge the feasibility of adapting video generation models into simulators.

  1. 机器之心 · 微信公众号82

    X-Era AI opensources 4D world action model E0.5, real-robot success rate surpasses π0.5

    X-Era AI has open-sourced its native 4D world action model E0.5, achieving overall average success rates of 85.0% and 88.3% on the AgileX Piper dual-arm robot and the Zerith H1 humanoid robot, respectively, 13.3 and 26.7 percentage points higher than π0.5.

    Why it matters: The original text provides real-robot success rate comparisons between E0.5 and π0.5 on dual-arm and humanoid platforms, which can be used to assess the manipulation generalization performance of the 3D geometric backbone approach.

  2. 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.

  3. 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. 简智机器人 · X67

    GenRobot.AI's two open-source datasets enter Hugging Face robotics dataset trending top five

    GenRobot.AI announced that both of its open-source datasets, Gen-HumanWholeBody and Gen-HumanEgo, have entered the top five of the Hugging Face robotics dataset trending list. Gen-HumanWholeBody is a first-person whole-body manipulation dataset containing 600 hours of whole-body manipulation data, 16,976 real-world scenes, and 1,281 human skills, with whole-body MPJPE of about 3 cm; the authors say it targets whole-body motion learning, human-to-robot transfer, and embodied foundation model research.

    Why it matters: Two open-source datasets entering the top five of the Hugging Face robotics dataset trending list can serve as a reference for observing the level of attention to whole-body manipulation data.

  1. The Humanoid Hub76

    Ropedia releases open-world model W0, trained on about 10,000 hours of first-person human video

    Ropedia released the open-world model W0, trained on about 10,000 hours of first-person human video. A single set of weights can predict the first-person view of humans, robotic arms, and humanoid robots (Unitree G1, Fourier GR-1), using a shared camera-plus-skeleton representation; adding a new robot requires only its URDF.

    Why it matters: The original article provides the training data scale, the cross-embodiment transfer method, and measured comparisons, so readers can judge the world model's actual impact on robot policy training workflows.

  2. 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.

  1. AI前线 · 微信公众号78

    Google releases Gemini 4 Argon physical reasoning model, connecting the full robotics R&D chain

    Google has released Gemini 4 (Argon), positioned as a general physical reasoning model focused on interaction with the physical world, covering the full robotics lifecycle from control execution and simulation reconstruction to data annotation and self-improvement. On the ERQA embodied reasoning benchmark, Argon scored 87%, surpassing Claude Opus 5.5 (82.3%) and GPT-6 Astra (81%); in the AA-Omniscience test, its hallucination rate was 15%, lower than the 51% and 59% of the latter two.

    Why it matters: The article breaks down the model's specific uses and measured figures across the robotics R&D chain in four layers: control, simulation, data quality inspection, and self-improvement.

  1. Xbotics具身智能实验室 · 微信公众号76

    Shanghai AI Lab evaluates GPT-6-Astra's capability boundaries as a robot policy on EBench

    Shanghai AI Laboratory's embodied evaluation team used EBench to evaluate GPT-6-Astra as a robot policy directly, without task-specific post-training, across 26 tasks and 510 total executions. Its average task success rate of 46.7% ranked third, and its average task score of 0.654 ranked second; OpenWAM-α led with a 55.3% success rate.

    Why it matters: The benchmark quantifies the capability gap between general large models and post-trained policies, helping readers judge which task lengths and manipulation precisions each approach suits.

  1. 深圳市机器人协会 · 微信公众号70

    HKU MMLAB releases PhysicalRSI 1.0, tops RoboDojo and sharply cuts token costs

    The University of Hong Kong's MMLAB jointly released PhysicalRSI 1.0, a self-evolving embodied intelligence system baseline, achieving a score of 36.27 and an average task success rate of 31.38% on RoboDojo, ranking first.

    Why it matters: The material provides the specific mechanisms of the self-evolving baseline and comparison data with general large models, allowing readers to understand the causes of shortcomings in precision manipulation and paths for improvement.

  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. Dexmal 原力灵机 · 微信公众号73

    Force Spirit DM0.5 Tops All Four RoboColiseum Leaderboards

    Force Spirit announced that DM0.5 has topped all four RoboColiseum leaderboards (instruction following, spatial understanding, perturbation adaptation, and general manipulation), making it the only model on the leaderboards to rank first in every evaluation.

    Why it matters: The original article lists DM0.5's specific scores on multiple leaderboards and its inference latency reduction data, which can be used to assess the practical effectiveness of its pretraining approach.

  1. 自变量机器人 · 微信公众号75

    X Square Robot releases WALL-SS next-scale autoregressive world model

    X Square Robot has released WALL-SS, a next-scale autoregressive world model that uses a coarse-to-fine, layer-by-layer refinement generation architecture, breaking away from the traditional diffusion-model approach of generating video in a single segment. In testing, its action-following score was 0.29, the only nonzero score among the compared models; the overlap between generated trajectories and action instructions rose from 0.251 to 0.539; streaming inference can reach 60 seconds of footage without collapse; and in closed-loop policy consistency tests across 6 tasks and 5 training stages, the success-rate agreement between virtual and real-world tasks reached 93%.

    Why it matters: The source gives three quantitative metrics and the architecture design, which readers can use to assess how feasible the world model is as a policy testbed.

  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. 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. 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.

  1. X Square Robot/自变量机器人67

    X Square Robot releases cross-modal embodied action tokenizer X-Tokenizer, with 13.5% gain in multimodal alignment and 8.25% gain in long-horizon tasks

    X Square Robot has released X-Tokenizer, a cross-modal embodied action tokenizer built on a semantic residual quantization (SRQ) architecture and pretrained on 17 robotic arm series and 2.4 million trajectories (including 2 billion action frames). Compared with FAST, it improves multimodal alignment by 13.5% and long-horizon task performance by 8.25%, and scores 82.8 on RoboTwin 2.0; in real-robot tests it outperforms comparison methods such as FAST and 4-level RVQ.

    Why it matters: The original text presents X-Tokenizer's method design and comparative experimental data, letting readers understand the action tokenizer's multimodal alignment approach and its reusable effects.

  1. HUMANOID63

    KinetIQ Ascend uses real-world reinforcement learning to boost humanoid robot manipulation throughput and success rate

    HUMANOID has launched KinetIQ Ascend, an end-to-end visual reinforcement learning method that operates under production deployment conditions, extending its KinetIQ framework to let robots improve manipulation skills through trial and error rather than relying solely on human demonstration; the company says this is the first such method publicly demonstrated on production-line humanoid robot manipulation.

    Why it matters: The original text provides specific throughput and success-rate figures for three industrial tasks and notes a pattern of predictable performance improvement with training duration, making it useful for assessing the practical value of real-world reinforcement learning on humanoid robots.