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#Robot models and learning
Oct 21Wed
  1. 机器人全球资讯 · 微信公众号58

    Sita Robotics completes angel round with Huawei Hubble and others participating, developing photonic crystal electronic skin

    Shenzhen Sita Robotics completed an angel funding round on October 13, with investors including Huawei's Hubble Investment, a fund under Shenzhen Capital Group's Shenzhen High-Tech Investment, and Gaoxuan Investment, among others; Duowei Capital served as exclusive financial advisor. The company was founded by Zhang Wei, a PhD born in 1998, and its team has an average age of about 23. It is combining flexible photonic crystals with photosensitive devices to develop flexible electronic skin less than 1 mm thick with a sensing density exceeding 10,000 points/cm².

  2. 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.

  4. 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.

  5. 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.

  6. 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.

  7. Xbotics具身智能实验室 · 微信公众号82

    RLinf v0.4 released: open-source training framework upgrade for embodied reinforcement learning

    RLinf, an open-source training framework for embodied reinforcement learning developed by Tsinghua University, Infinigence AI, and the Beijing Zhongguancun Academy, has officially released v0.4, with updates across four areas: a Lego-style real-machine system, real-machine post-training algorithms, full-modality model support, and a domestic computing power foundation.

    Why it matters: It outlines the framework's evolution from simulation to real-machine training, helping readers understand the capability boundaries of the embodied reinforcement learning engineering stack.

Oct 20Tue
  1. 车东西42

    Chery unveils 2035 technology strategy: CheryMind large model, Robotaxi, eVTOL and electric agricultural machinery

    At the 2026 Global Innovation Conference, Chery unveiled its CheryMind vertical foundation model technology base and showcased products including a native front-installed Robotaxi, two eVTOL aircraft, the Wantu embodied agricultural machinery and the Mojia robot. Chery said its Robotaxi goal is to deploy 100,000 units by 2030, and that the pilot line for its Rhino S solid-state battery will be put into use in 2027.

  2. 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.

  6. 深蓝AI · 微信公众号72

    Zhejiang University's Gao Fei team releases CrossFly benchmark: evaluation of cross-indoor-outdoor interactive navigation for drones and a hierarchical VLA baseline

    The Gao Fei team at Zhejiang University, together with Weifen Zhifei, launched the CrossFly benchmark, building 23 reconstructed real-world scenes and 15,263 complete trajectories, covering three task types: indoor, outdoor, and indoor-outdoor cross-domain, accompanied by a hierarchical VLA baseline, CrossFly-Agent; the full simulation dataset, evaluation protocol, and baseline code are all open source.

  8. HuggingFace Daily Papers69

    Learning from Fleet Experience: Improving Robot Manipulation Policies with Shared Fleet Experience

    The paper proposes the collective experience learning (CEL) paradigm, in which deployed robots share execution experience to improve a common policy without collecting additional expert demonstrations or corrective actions. It uses failure-guided experience retrieval fine-tuning (FERT) to retrieve relevant trajectory segments from the fleet's shared successful experience as supervision, and a fault-tolerant asynchronous training system (FATS) to coordinate experience collection, policy optimization, and distribution.

  9. HuggingFace Daily Papers50

    Vision-Language-Delta-Action: Using Compact Deltas as an Executable State-Transition Interface for Robotic Manipulation

    VLDA proposes modeling task-relevant state transitions with compact Delta variables, replacing VLA's direct observation-to-action mapping and the full future-state prediction of world-action models. It learns Delta under a unified objective via LT-Filter and GT-Anchor, and DDAR injects Delta into the action denoising process as a structured seed. Experiments implemented in the open-source training framework LoongForge show that VLDA improves long-horizon consistency, robustness, and generalization.

  11. HuggingFace Daily Papers62

    AnyGoal: Evaluating World Models' Compositional Generalization Beyond Expert Trajectories

    The study introduces the AnyGoal benchmark and open-source library to evaluate world models' compositional generalization on goal-shifted task variants. The task design ensures that individual state factors and actions remain within the training support, but their combinations are underrepresented in expert trajectories. Experiments across multiple environments and world model families show that current models consistently fail under these recombination scenarios, and the authors further analyze where failures occur at the dynamics and representation levels.

  12. HuggingFace Daily Papers53

    TokenWorth: Learning which memories are worth keeping under resource constraints

    The paper proposes TokenWorth, a lightweight online memory framework that learns query-relevant memory value from sparse execution feedback and applies it consistently to memory retrieval and bounded memory management. Across three LifelongAgentBench environments and in both local and API-based deployments, TokenWorth achieves the highest task success rate among the evaluated methods while using the fewest output tokens. The code is publicly available at https://github.com/AIGNLAI/TokenWorth.

  13. HuggingFace Daily Papers57

    Fréchet Forcing: Training Long-Horizon Streaming World Models with Short Video Clips

    Fréchet Forcing proposes a fine-tuning method that keeps streaming video world models stable during long-horizon generation beyond the training length and lets them continue to follow action commands. The method applies a flow-matching loss where self-generated history meets real data, constrains context fidelity with a Fréchet distance centered on the real clip itself, and enforces visual realism with a Fréchet distance on real frames in a frozen perceptual space. All three constraints come from short video clips, with no teacher model, online judge, or long videos needed.

  14. HuggingFace Daily Papers57

    ScaleNav: Internet Video Pretraining for Language-Conditioned Local Navigation

    ScaleNav proposes a unified world-action framework that achieves language-conditioned local navigation through three-stage training: first, instruction-conditioned video pretraining on internet first-person videos to learn how observations evolve as the camera approaches a target; then, aligning predicted executable action chunks using action-labeled simulation data; and finally, jointly fine-tuning all trainable components.

  15. 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.

  16. Reddit r/robotics38

    Tabletop battle simulation of two open-source Jumper robot crabs in MuJoCo

    Zymbiotek created a tabletop battle simulation in MuJoCo of two open-source Jumper robot crabs for a KingKong Robotics competition. The motions are physics-simulated based on measured servo torque curves, and the dance is a reinforcement learning policy that can run on the real robot. The counter uppercut is a real simulated motion, with only the final flip being animation—testing showed one Jumper cannot flip another.

  17. 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.

  20. HuggingFace Daily Papers72

    AgenticRoboRL: Improving Robot Planners with Coding-Agent Post-Training Methods

    AgenticRoboRL proposes a framework for post-training robot planners across different robot execution harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning, and harness-specific adapters connect planner interaction records and task outcomes to a unified RL training pipeline while keeping execution components fixed.

  21. Beijing Humanoid Robot Innovation Center58

    Renmin University GeWu-Lab and Beijing Humanoid Innovation Center to livestream Robo-ValueRL millimeter-level VLA reinforcement learning solution

    Renmin University of China's GeWu-Lab and the Beijing Humanoid Innovation Center will livestream a presentation of the Robo-ValueRL millimeter-level VLA reinforcement learning solution on October 21, 2026 at 19:00 Beijing time, with talks by Hu Di and Xia Wenke.

  23. 机器人产业应用 · 微信公众号67

    RSI Gains Traction: Robot Deployment Is Becoming the Starting Point for the Next Round of Training

    The article analyzes the rise of recursive self-improvement (RSI) in embodied intelligence, noting that Anthropic's definition requires AI to autonomously develop successor systems, while most current systems remain bounded self-improvement. 1X established the World Model Lab to close the loop from pretraining to real-world data; Motus2 uses three interfaces—Policy, Simulator, and Evaluator—to achieve bounded self-evolution; and Pudu's PuduFM 1.0 relies on more than 130,000 deployed robots to build a data flywheel.

  24. Tech Xplore Robotics62

    Seoul National University's Dynin-Robotics robot foundation model guides actions by predicting future scenes, achieving 78.4% average success rate in real-world operation

    Professor He Jiong's team at Seoul National University's AIDAS Lab proposed Dynin-Robotics, a robot foundation model that unifies language, images, video, and robot actions in a single model, generating and selecting actions by predicting future scene images after an action; the paper has been accepted by NeurIPS 2026.

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. 钛媒体62

    Niuwa Robotics' Yang Ruigang: Why the Last Few Hundred Meters of Unmanned Delivery Struggle to Reach the Door

    A TMTPost interview and analysis of Niuwa Robotics founder Yang Ruigang notes that after a decade of unmanned delivery development, China's logistics unmanned vehicle fleet exceeds 35,000 units and more than 200 cities have opened road rights, yet the last few hundred meters—entering residential compounds, boarding elevators, and delivering to the doorstep—remain unsolved. Most explorations at home and abroad, including JD.com, Cainiao, and Amazon Scout, have largely exited. The core reasons are ambiguous rules for private spaces, high deployment costs, and the need for human fallback for exceptions; no company has ever made this account profitable.

  4. Xbotics具身智能实验室 · 微信公众号72

    Xiamen University HumanPlus, CMU, and MIT Launch CLOTHO, Embedding IMUs in Loose Clothing for Eight-Hour Full-Body Motion Capture

    The HumanPlus lab at Xiamen University, together with researchers from MIT and CMU, has launched CLOTHO, a full-body motion capture system that integrates 10 IMUs into loose clothing. The paper was accepted to the SIGGRAPH Asia 2026 journal track and will be published in ACM Transactions on Graphics.

  5. 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.

  6. PaperWeekly · 微信公众号56

    Central South University and other institutions release the first systematic review of system-level agent distillation

    A research team from Central South University, the Beijing Academy of Artificial Intelligence, Hong Kong Polytechnic University, Beijing University of Posts and Telecommunications, and other institutions jointly released a 57-page systematic review of system-level agent distillation. The paper is titled Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses, and is available at https://arxiv.org/abs/2609.36630.

  8. 雷科技78

    WorldOracle world-model evaluation platform launched, using real-robot testing to fact-check the industry

    On October 16, WorldOracle, an action-driven world-model evaluation platform jointly completed by researchers from AgiBot, NVIDIA, Princeton University, the University of Texas at Austin, and the University of Toronto, was officially released, opening evaluation submissions to developers.

    Why it matters: The evaluation compares world-model predictions frame by frame against real execution records, revealing a common problem in which robotic arm motion is replicated but object states are systematically hallucinated, providing concrete reference points for judging model usability.