Skip to content
#Model releases
Oct 21Wed
  1. NVIDIA40

    NVIDIA and UW Medicine release open protein design model RFD4-Proteina; Siemens Healthineers advances adaptive radiotherapy

    NVIDIA and the UW Medicine Institute for Protein Design launched the open model RFD4-Proteina, combining programmable protein design with search capabilities. In four benchmark tests reported in a research draft—protein binding, small-molecule binding, enzyme scaffolds, and DNA binding—computational design success rates and generation throughput were both higher than the evaluated baselines.

  2. 36Kr Embodied Intelligence58

    Nvidia expands open model lineup with Cosmos 3.5 world model and more

    Nvidia announced an expansion of its open model lineup, introducing the Nemotron Retriever retrieval model, the Kumo structured enterprise data model, the Cosmos 3.5 world model, the Alpamayo 2 Edge autonomous driving model, the BioNeMo protein design model, and the Ising quantum computing model, among others. In a benchmark covering proteins of 200 amino acids, BioNeMo RFD4-Proteina generated designs about 13 times faster than RFdiffusion3.

  3. 量子位62

    Qianjue Technology releases predictive world model Polibrain

    Tsinghua-affiliated embodied intelligence company Qianjue Technology released the predictive world model Polibrain at the 2026 Physical AI and World Models Conference. It focuses on learning physical laws and cross-embodiment reuse; rather than generating complete future frames, it predicts state changes in a task-relevant latent representation space, forming a closed loop of perception, decision-making, execution, and feedback.

  6. arXiv cs.RO56

    ACE-Brain-0.6 Released: Scaling Spatial Perception of a 9B Embodied Model with Verifiable Grounding Supervision

    The research team released ACE-Brain-0.6, which uses a unified data pipeline to combine robot manipulation recordings with complementary visual data from multiple sources, automatically annotates them, and converts the result into verifiable grounding supervision; the model was obtained by fine-tuning Qwen3.5-9B on approximately 1.5 million samples. ACE-Brain-0.6 outperforms GPT-6 Astra on four of seven public grounding benchmarks, with an overall average score of 75.41 versus 73.68; under a fixed fine-tuning setup, overall grounding performance improves steadily as the dataset expands to cover more grounding scenarios.

  7. OFweek Robotics65

    Geek+ Gravity Control uses natural language for runtime correction, raising OOD task success rate from 5% to 95%

    Under the Gravity framework, Geek+ embodied intelligence proposes the Gravity Control causal intervention model. Without training the original policy model or adding new demonstration data, it uses mechanistic interpretability to uncover the model's internal action regulation mechanisms and intervenes on relevant latent features at inference time to achieve runtime correction.

  8. 钛媒体34

    Edge AI Daily Morning Brief (October 21)

    An evaluation by SenseTime together with teams including Daxiao Robotics and Nanyang Technological University shows that GPT-6-Astra came close to full marks on several of 12 simulation benchmarks (100% on LIBERO-Long++, 98.8% on SIMPLE), but after deployment on 8 real robot platforms, all 11 attempts to plug in a plug failed and folding clothes ran into trouble. Its high scores also depend on two "boosters": in-context learning (ICL) and relaxing the action step limit to 10x; on the long-horizon household benchmark BEHAVIOR, its success rate was only 37.0%.

  9. 简智机器人 · X61

    Genrobot reposts congratulations to Dream Labs on releasing robot foundation model Dream-1

    Genrobot.AI reposted and congratulated Dream Labs on launching Dream-1, saying it is a launch partner for Dream-1. The quoted content says Dream Labs has exited stealth and released Dream-1, a robot foundation model that can control dual-arm robots it has never seen before, complete tasks that were not demonstrated, and work in scenes it has not visited, at a price of 3 USD per hour, with the Dream API opening to everyone that day.

  10. 机器人全球资讯 · 微信公众号61

    Nature Will releases Natura-1 model, demonstrating dexterous tasks such as robot hands wrapping wontons

    Nature Will recently released a demonstration video of the Natura-1 model, in which robotic hands, without human assistance, completed tasks such as wrapping wontons, packing express deliveries, brewing tea, applying phone screen protectors, and writing Chinese calligraphy with a brush. Natura-1 is a 50-billion-parameter mixture-of-experts (MoE) model, with data processing covering nearly 1 million hours of first-person human interaction data. It can drive a 20-degree-of-freedom dexterous hand within a 100-millisecond control loop and follow complex natural language instructions to generalize in unseen scenarios.

  12. Reddit r/robotics38

    Cubic Doggo reinforcement learning standing stability training update

    Cubic Doggo used Gymnasium and SB3's PPO in MuJoCo to train standing while minimizing pitch and roll, and deployed the ONNX file back to MuJoCo, raising resistance to horizontal push from 3.5N to 4N. The second phase added "push recovery training," using horizontal force as domain randomization under a curriculum schedule, but after two weeks of training the improvement was limited. Next steps are balancing on fixed and varying slopes, then deployment to Gazebo and a physical robot.

  13. Reddit r/robotics40

    Dream Labs releases Dream-1; real-world zero-shot manipulation occasionally loses control

    Dream Labs released Dream-1 yesterday. A user deployed it on a robotic arm, saying it is very fast and can perform tasks zero-shot. In testing, when asked to move an apple, it instead grabbed a red block and did not stop, showing occasional random behavior. The publisher has made Dream-1 publicly available for people to try.

  14. APPSO · 微信公众号75

    VAST Open-Sources Real-Time World Model EdenLive and Companion Tool EdenGolem

    VAST has open-sourced EdenLive, a real-time generative renderer in the Project Eden series. The upstream interaction system maintains game rules and state, while EdenLive handles real-time image rendering. It supports white-box models, 2D/3D scenes, or real footage as references, and changing the first frame and prompt can alter the look among photorealistic, hand-drawn, cartoon, and other styles.

Oct 20Tue
  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.

  4. ModelScope Robotics69

    Xingchen releases and open-sources intent understanding model Xing4.0-29B-Intent

    The Xingchen General Artificial Intelligence Laboratory of China Telecom Artificial Intelligence Technology Co., Ltd. has released and open-sourced Xing4.0-29B-Intent, a model dedicated to intent understanding, and has simultaneously opened 2.753 million supervised fine-tuning training corpus entries, model weights, and inference images, with the code licensed under Apache 2.0.

  9. PaperWeekly · 微信公众号74

    SenseTime SenseNova-RoboRSI unified Harness raises GPT-6-Astra success rate on RoboDojo from 15.83% to 54.17%

    A joint research team from SenseTime, Nanyang Technological University and other institutions has introduced the SenseNova-RoboRSI unified Harness, which raises GPT-6-Astra's manipulation success rate on 42 RoboDojo tasks from 15.83% in the baseline configuration to 54.17%, with model parameters unchanged.

  10. 简智机器人 · X61

    GenRobot Congratulates Dream Labs on Launching Robot Foundation Model Dream-1

    Dream Labs has launched Dream-1, a robot foundation model that can control never-before-seen dual-arm robots, complete tasks that were not demonstrated, and adapt to unfamiliar environments. The Dream API opened to everyone the same day, starting at $3/hour. GenRobot said it provided human data collaboration for Dream-1 and called this another breakthrough in zero-shot research.

Oct 19Mon
  1. CVHub · 微信公众号74

    XPeng and multiple universities propose 4B perception foundation model GroundingPI, unifying point, box, and text localization and validating action transfer

    Researchers from XPeng and Peking University, the University of Hong Kong, UC Berkeley, Princeton University, National University of Singapore, Tsinghua University, and Hong Kong University of Science and Technology (Guangzhou) jointly proposed a 4B perception foundation model, GroundingPI, which unifies point selection, boxes, text, document regions, and interface controls into a single spatial token output protocol. The architecture uses a MoonViT-V2 vision encoder and a Qwen3-4B language backbone, and training includes supervised fine-tuning and GRPO reinforcement post-training.

  2. 机器人前瞻 · 微信公众号73

    Ropedia and multiple universities release cross-embodiment mobile manipulation world model Ropedia W0

    Singapore-based embodied intelligence startup Ropedia, together with Nanyang Technological University, Duke University, UC San Diego, and Tsinghua University, released the cross-embodiment mobile manipulation world model Ropedia W0, which uses a unified action representation to predict first-person future scenes after humans, robotic arms, and humanoid robots perform actions.

  3. 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.

  4. 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.

  5. 36Kr Embodied Intelligence42

    MiniMax M3.1 Flash launches with across-the-board performance gains, 3x faster speed, and 70% lower price

    MiniMax has officially released its new-generation Flash model, M3.1 Flash, with online inference speed nearly 3 times faster and usage costs reduced by 70%. The Space Bunny model, which previously handled over 200T tokens anonymously, was in fact an early version of M3.1 Flash. The model delivers generational improvements in agentic tasks, coding, Computer Use, and cybersecurity capabilities. MiniMax has teased that a Pro version pursuing a higher intelligence ceiling will be released soon.

  6. 深蓝AI · 微信公众号62

    Daxiao Robotics releases ACE-Brain-0.6: 9B model beats GPT-6 Astra on six Grounding benchmarks

    Daxiao Robotics released the embodied spatial perception model ACE-Brain-0.6, built on Qwen3.5-9B with a frozen vision encoder and supervised fine-tuning of the language model parameters. It surpasses GPT-6 Astra on six of eight Grounding benchmarks, leads with an average of 74.53 points across seven public benchmarks versus Astra's 73.68, and scores 94.13 on RobotGrounding versus 91.10.

  7. arXiv cs.RO64

    GroundingPI releases 4B grounding foundation model, averaging 73.68% across 34 benchmarks

    GroundingPI is a 4B-parameter grounding foundation model that outputs points and boxes as quantized coordinates in a shared vocabulary; its training combines multimodal and spatial pretraining, supervised fine-tuning, and GRPO reinforcement learning. It averages 73.68% across 34 grounding benchmarks and 44 baselines, above GPT-6 Astra's 71.54%.

  8. 仙工智能58

    Seerbotics demonstrates VLA model completing Tower of Hanoi task: still able to continue solving after base is pushed and discs are scrambled

    Seerbotics demonstrated its self-developed VLA model driving a robotic arm to complete the seven-move sequence of a three-disc Tower of Hanoi. When an observer slides the base to change its overall position or rearranges the discs into an arbitrary intermediate state, the robotic arm can still re-identify the current configuration, determine the next move, and continue completing the remaining task. Trained on only 60 demonstration trajectories, the model can generalize across changes in object position, color, and shape. The Seerbotics robot "brain" consists of three layers—model, cerebellum, and nerve—responsible respectively for scene understanding and decision-making, motion planning and execution, and a unified interface connecting sensors and joint actuators.

  9. HuggingFace Daily Papers62

    ACE-Brain-0.6 released: scaling embodied foundation models with verifiable spatial grounding supervision

    ACE-Brain-0.6 combines robot manipulation recordings with complementary visual data from multiple sources through a unified data pipeline; after automatic annotation, the data is converted into verifiable grounding supervision, and Qwen3.5-9B is fine-tuned on about 1.5 million samples to produce the model. It outperforms GPT-6 Astra on four of seven public grounding benchmarks, with an overall average score of 75.41 versus Astra's 73.68. Under a fixed fine-tuning setup, aggregate grounding performance improves steadily as the dataset expands to cover more grounding scenarios.

  10. 机器人全球资讯 · 微信公众号62

    Westlake Robotics WR1 demonstrates full-body coordinated autonomous clothes folding and cross-area household chores

    Westlake Robotics has upgraded its general-purpose brain WR1, adopting a dual pretraining architecture of a general-purpose large brain and cerebellum, integrating a world model with VLA to generate action sequences, and executing full-body control through its self-developed general action model GAE. In the demonstration, WR1 completed a continuous task sequence: taking clothes from a drying rack, folding pants with two machines working together, and across the kitchen, living room, and bedroom, fetching corn, storing dolls, and tidying a quilt. The system introduces long- and short-term motion state review and a real-time action chunking mechanism to reduce pauses and abrupt changes in long-horizon tasks.

Oct 18Sun
  1. 量子位74

    VAST open-sources real-time world model EdenLive, over 24 FPS at 720P on a single RTX 5090

    VAST has released and open-sourced its real-time world model EdenLive, opening up the model weights, inference code, technical report, and an end-to-end demo. According to VAST, on a single consumer-grade Nvidia RTX 5090 GPU at 720P, the 5B model generates at over 24 FPS, can generate continuously for minutes at a time, and has end-to-end latency in the hundreds of milliseconds.

  2. 36Kr Embodied Intelligence47

    StartLux-Decision-v1.1-27B Instant Tops Hugging Face Decision Index 0.3.2

    StartLux-Decision-v1.1-27B Instant, from Chinese AI startup StartLux (Yuandian Xinghui), ranked first worldwide on the Hugging Face Decision Index 0.3.2 with a score of 69.1, placing it at the top among 173 publicly reproducible models. It leads the second-place model by nearly 3 points and is the only model on the list to exceed 69 points. After releasing its first-generation model in late September, StartLux set a new best result in just over ten days, maintaining its position as the global SOTA in decision models.

  3. Guyuehome46

    Light-MER: Compressing an 8B Emotion Teacher into an 855M Student Using Hidden-State Geometry Rather Than Answers

    Light-MER is a multimodal emotion recognition compression work by the University of Glasgow, the Institute of Computing Technology of the Chinese Academy of Sciences, and Shandong University. Accepted to the ACM MM 2026 main conference, it compresses an 8B teacher model into an 855M student model, with the training signal drawn from hidden-state geometry rather than answers. The work targets the deployment cost of generative multimodal emotion recognition, with the goal of enabling the model to output paragraphs that explain emotions.

  4. Guyuehome51

    TD-MPC2: Model-Predictive Reinforcement Learning Control Practice with Latent World Models

    The author uses official pretrained weights to drive a quadruped canine model through real running inference on a local CPU, reconstructing item by item the mathematical construction of the latent world model, the consistency learning objective, symlog two-hot encoding, and MPPI-style sampling-based planning, and records observed phenomena such as planning distribution contraction and the gap between estimates and realized outcomes.

Oct 17Sat
  1. 机器人产业应用 · 微信公众号74

    How far has Generalist AI GEN-1.5's physical prompting paradigm progressed?

    Generalist AI released GEN-1.5 on August 19, 2026; it can attempt new tasks from a single 3-12 second task demonstration without gradient updates, achieving a one-shot average success rate of 59%±10% across 10 short manipulation tasks, and this can be raised to 83%±9% by adding about 5 minutes of data and performing 10 gradient updates.

Oct 16Fri
  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.