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#Robot models and learning
Oct 30Fri
  1. 未来机器人网 · 微信公众号72

    Daxiao Robotics and NTU Release Mundo World Model

    Daxiao Robotics, in collaboration with Nanyang Technological University's S-Lab, has released Mundo, a world model that reconstructs 1 to 12 real-scene images into an explorable, physically interactive 3D simulation environment. The model has built-in physical properties, with MuJoCo computing object motion under gravity and contact constraints, and it supports training-data generation and policy testing for tasks such as navigation and pick-and-place. The team has reconstructed a hotel floor and laundry area for training service robots on navigation and interaction tasks.

  2. 移动机器人产业联盟 · 微信公众号25

    Revaluing Hangcha Through Physical AI: Can AI-Fork and LogiMind Unlock the Next Round of Competition in Unmanned Forklifts?

    Hangcha Group is positioning itself in physical AI with its AI-Fork full-series intelligent forklift robots and the LogiMind industrial embodied intelligence large model, and plans to launch new products at the fourth Global Dealer Conference on November 10. Global forklift stock stands at about 12 million units, while unmanned forklift penetration in 2025 is only around 2%; Hangcha aims to lower deployment barriers through environment understanding and task planning capabilities. Its technology portfolio also extends to Hangcha Guazi's latent transport robots, bin robots, and the X1 wheeled humanoid robot.

  3. Tech Xplore Robotics40

    Armoring the caterpillar: Study reveals design clues for soft crawling robots moving on smooth surfaces such as glass

    A team from the University of Naples Federico II and the University of Palermo used a spring-mass crawling model to analyze the movement of Pieris brassicae caterpillars on wood, steel, slate, and glass, finding that surface roughness is strongly correlated with crawling performance; glass is difficult to traverse because its microscopic roughness is insufficient for the prolegs to grip.

  4. Tech Xplore Robotics75

    University of Pennsylvania proposes Fast Feature Field, providing a general-purpose robotic vision representation for event cameras

    An engineering team at the University of Pennsylvania has proposed Fast Feature Field (F³), which converts the tens of millions of signals per second from event cameras into a general-purpose representation usable by existing computer vision software. The paper was published in Science Robotics.

  5. 高工咨询 · 微信公众号72

    Lead Robot Releases Shipbuilding World Model "Dingfengbo"

    At the 2026 China Marine Economy Expo, Lead Robot released "Dingfengbo," a world model for shipbuilding. It takes design drawings, process documents, and bills of materials as unified inputs, parses the engineering logic among them, breaks an entire ship down into blocks, sub-assemblies, sub-assemblies and parts, and reconstructs the 3D structure. This frees robots from reliance on manual teaching, enabling them to perceive autonomously, decompose tasks, and decide how to work.

  6. 机器之心 · 微信公众号72

    Fudan and NTU propose SimpleVLN, using a native multimodal large model to set a new SOTA in vision-language navigation

    A team from Fudan University and Nanyang Technological University, Singapore, proposed SimpleVLN, which performs vision-language navigation without changing the core structure of the multimodal large model or adding dedicated navigation modules, relying only on historical frame selection, action-chunk decision-making, and three-stage training with SFT, DAgger, and reinforcement learning.

  7. 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.

  8. 机器之心 · 微信公众号88

    Tsinghua, AgiBot and Infinigence AI propose Collective Experience Learning (CEL), validated on 96 real robots for scalable gains

    Tsinghua University, AgiBot and Infinigence AI jointly proposed the Collective Experience Learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone-packaging task, the trained policy completed 120 tests on two robots of the same model that did not participate in training, raising the success rate from 79.2% to 98.3% and reducing failures from 25 to 2.

    Why it matters: The 96 real robots validated the scalable gains of collective experience sharing, giving readers a way to understand one path toward robots autonomously accumulating experience instead of relying on human demonstrations.

  9. 机器之心 · 微信公众号76

    PokeBot releases embodied API RoboAPI, world-action model Poke-Aurora debuts

    PokeBot released its first model product, RoboAPI, in the early hours of October 30. It is a publicly accessible, out-of-the-box embodied API: after developers connect and adapt a robotic arm, they need neither to collect data nor train models, and can issue commands with a single prompt to execute tasks the model was not specifically trained on.

    Why it matters: Readers can learn how a publicly accessible embodied API hands zero-shot generalization capability over to external developers for testing, as well as its success-rate data from its own evaluation.

  10. 机器之心 · 微信公众号75

    Shanghai Jiao Tong University and others propose VaM: using value prediction for task-progress memory, achieving over 95% success rate in 4-hour continuous carton assembly

    Shanghai Jiao Tong University, Shanghai Innovation Institute, and others propose Value as Memory (VaM), which feeds the recent prediction sequence of a value model into the action policy as task-progress memory, combined with an offline-to-online real-robot reinforcement learning pipeline. In a carton assembly task, VaM ran autonomously and continuously for 4 hours with a task success rate above 95%. Ablations show that compared with a version without the value cache, the final task success rate improved by 29.4%; the online RL approach improved average success rate by 32.8% across three tasks and reduced the real-world time cost per gradient step by 57.8% on average.

  11. 机器人产业应用 · 微信公众号58

    Boyin Hechuang officially opens Suzhou headquarters and joins Bosch supplier system

    On October 29, Boyin Hechuang, jointly incubated by Boyuan Capital and Galaxy General, officially opened its 10,000-square-meter Suzhou headquarters, covering the full chain from R&D, simulation testing, scenario validation, and small-batch trial production to customer delivery, and has entered the Bosch supplier system. The company says its BW10 series, equipped with the VLA model, achieves efficiency of about 800–1,000 items per hour in logistics sorting, and that BW10-Lite has been deployed in the in-plant logistics scenario at United Automotive Electronic Systems' Shanghai plant. Under its plan, the company aims to gradually raise annual production capacity to 4,000 units by 2027, and its German subsidiary is in the preparatory stage.

  12. 36氪 · 微信公众号58

    Unitree Shares Rebound Over 70%, as Valuation Logic for the Embodied AI Industry Shifts

    Dingjiao One analysis says Unitree Technology's share price has rebounded by more than 70% cumulatively in recent days, with market capitalization recovering to over 280 billion yuan. However, its first post-IPO financial report, released on October 28, shows Q3 revenue of 665 million yuan, up 69.93% year-on-year, while non-GAAP net profit was 105 million yuan, down 17.42% year-on-year.

  13. CVer · 微信公众号71

    Fudan and NTU propose SimpleVLN: native multimodal large model sets new SOTA in vision-language navigation

    A team from Fudan University and Nanyang Technological University, among others, proposed SimpleVLN, which performs vision-language navigation without changing the core structure of a multimodal large model or adding dedicated navigation modules, relying only on observation filtering, action-chunk decision-making, and three-stage training with SFT, DAgger, and reinforcement learning.

  14. 新智元 · 微信公众号74

    Daxiao Robotics and NTU S-Lab Release Interactive World Model Mundo

    Daxiao Robotics, in collaboration with Nanyang Technological University's S-Lab, has released Mundo, an interactive world model built for specific environments that integrates 3D scene reconstruction, physics simulation, and video generation. The system uses scene code to record object weight, center of mass, and friction coefficients, computes motion and collisions with a 960Hz MuJoCo physics engine, and then renders visuals combined with on-site photos; inputting 1 to 12 phone photos is enough to reconstruct a 3D space that supports pushing, pulling, and collisions.

  15. 深蓝AI · 微信公众号38

    A Detailed Guide to 5 Mainstream Robot Path Planning Algorithms: MPC, Reinforcement Learning, Graph Search, and More

    Following the thread of "how to solve problems in different environments," the article reviews five categories of path planning paradigms in embodied intelligence: graph search (Dijkstra, A*, Hybrid A*), sampling methods (RRT, PRM), artificial potential field (APF), optimal control (MPC), and reinforcement learning and data-driven methods.

  16. Xbotics具身智能实验室 · 微信公众号88

    Infinigence AI, Tsinghua and AgiBot propose collective experience learning (CEL) paradigm, validated on 96 robots

    Infinigence AI, together with Tsinghua University and AgiBot, proposed the collective experience learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone packaging task, a policy trained with CEL completed 120 tests on two robots of the same model that did not participate in training, with success rate rising from 79.2% to 98.3%; when the number of robots participating in learning increased from 16 to 96, the test success rate rose from 86.7% to 98.3%.

    Why it matters: Readers can learn about a training paradigm that lets robots share successes and failures and reduces reliance on human demonstration, as well as its validation results on 96 real robots.

  17. Xbotics具身智能实验室 · 微信公众号62

    PokeBot launches embodied API RoboAPI, powered by world-action model Poke-Aurora

    PokeBot has officially launched RoboAPI, a publicly accessible embodied API that receives robot observations and state and predicts and outputs actions in near real time according to user instructions. It is free during the technical preview, and developers can apply via platform.pokebot.com.

  18. 帕西尼 · 微信公众号56

    CAS Institute of Automation proposes tactile-enhanced grasp-and-insertion framework, achieving 67% success rate at 0.05 mm clearance

    The team led by Cheng Long at the Institute of Automation, Chinese Academy of Sciences, proposed a tactile-enhanced full-pipeline learning framework for grasping and insertion, achieving a 67% success rate at 0.05 mm clearance and a 60% reduction in peak contact force; the paper was accepted to IROS 2026. The research uses the PaXini PX6AX GEN3 multi-dimensional tactile sensor as its core sensing hardware, integrated into a seven-axis robotic arm, covering five geometric shapes and three clearance levels of 1, 0.25, and 0.05 mm. Under the same conditions, the tactile-free IL+RL approach achieved a 52% success rate, while the pure imitation learning approach achieved 0.

  19. 古月居 · 微信公众号58

    Open X-Embodiment and RT-X: A Practical Guide to Cross-Embodiment Transfer for Large-Model Robot Policies

    This article provides a systematic walkthrough of cross-embodiment transfer practice with Open X-Embodiment and RT-X, covering RLDS data-format unification, the RT-X model architecture, pretraining, fine-tuning, and deployment on new robots. It presents RLDS conversion scripts, a PyTorch implementation of an RT-1-based model, and training techniques such as temperature sampling and embodiment-weighted loss, and it documents six categories of pitfalls, including slow data loading, poor cross-embodiment performance, and action-space mismatch, along with corresponding solutions.

  20. 雷科技40

    D-Robotics and Unitree Technology jointly launch embodied intelligence education solution, with Sunrise S100 strengthening the computing foundation

    D-Robotics and Unitree Technology exhibited together for the first time at the Changsha Education Equipment Exhibition and announced a strategic partnership, launching an embodied intelligence education solution with the Sunrise S100 computing box as the core computing foundation, combined with Unitree's Go2 quadruped robot and R1 humanoid robot. The solution includes 15 themed courses totaling 180 lessons, covering elementary through high school, and offers a two-level progression path (desktop level and real-robot level) plus 6 courses integrating competition and coursework. The Sunrise S100 integrates a CPU, BPU, and MCU within a single SoC, placing perception and decision-making and real-time motion control on the same chip for coordinated operation.

  21. 机器人daily · 微信公众号62

    CAS Institute of Automation and Paxini collaborative study: tactile-enhanced reinforcement learning achieves sub-millimeter precision assembly

    A team led by Cheng Long at the Institute of Automation, Chinese Academy of Sciences, published research at IROS 2026 using tactile-enhanced reinforcement learning to improve the success rate and stability of sub-millimeter precision insertion. The approach first uses Diffusion Policy imitation learning to reach the hole opening, then SAC reinforcement learning to complete the insertion, and handles real-world tactile noise through tactile baseline compensation and balanced sampling grouped by torque.

  22. 中科慧灵 · 微信公众号58

    CASBOT completes several hundred million yuan A++ funding round; CASBOT PA model enters footwear workstation validation

    CASBOT announced the completion of a several-hundred-million-yuan Series A++ funding round, jointly invested by institutions including Zhilai Investment, Yonghua Investment, Maotian Investment, Yuanhe Chenkun, Guoke Kezhi, and Luning Investment. The funds will be used for mass production and delivery of products, R&D and iteration of embodied brain technology for key scenarios, and market expansion.

  23. AgiBot38

    AgiBot proposes Worken framework for measuring effective robot work

    AgiBot has proposed a robot workload measurement framework called Worken, which multiplies three factors—the number of active robots, autonomous working hours, and output efficiency ratio—to estimate the effective intelligent work hours completed by robots in real deployments. The framework was proposed by co-founder, president, and CTO Peng Zhihui, and emphasizes distinguishing powered-on time from autonomous operating time, and measuring qualified output against task benchmarks.

  24. 36Kr Embodied Intelligence82

    AgiBot, Tsinghua, and Infinigence AI Release CEL Collective Experience Learning Paradigm, Achieving Autonomous Cluster Evolution with 96 Real Robots

    On October 30, AgiBot, together with Tsinghua University and Infinigence AI, released the collective experience learning paradigm CEL, which draws on the real operational trajectories of robot peers as its source of experience, accompanied by the FERT failure-guided experience retrieval fine-tuning algorithm and the FATS fault-tolerant asynchronous training system. Across five real-world tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—CEL-trained policies all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points. In the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%.

    Why it matters: Readers can learn about a collective learning paradigm that draws on the real trajectories of peers as its source of experience, as well as the success-rate changes in cluster training with 96 real robots.

  25. 3D视觉工坊 · 微信公众号88

    Infinigence AI, Tsinghua and AgiBot propose Collective Experience Learning (CEL), validated on 96 robots with 98.3% success rate

    Infinigence AI, together with Tsinghua University and AgiBot, proposed the Collective Experience Learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone packaging task, policies trained with CEL completed 120 tests on two robots of the same model that did not participate in training, with the success rate rising from 79.2% to 98.3%; when the number of robots participating in learning increased from 16 to 96, the test success rate rose from 86.7% to 98.3%.

    Why it matters: Fault-tolerant asynchronous training across a 96-robot real-machine cluster and failure-guided experience retrieval provide a reproducible system and scale-benefit data for Collective Experience Learning.

  26. 雷科技84

    AgiBot and Tsinghua Release CEL Collective Experience Learning Paradigm, Achieving Autonomous Swarm Evolution with 96 Real Robots

    AgiBot, together with Tsinghua University and Infinigence AI, released the collective experience learning paradigm CEL, which lets robots use the success and failure trajectories of their peers as learning material, with humans only responsible for judging outcomes. Across five real-world tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points; in the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%.

    Why it matters: AgiBot and Tsinghua released the CEL collective experience learning paradigm, validating with 96 real robots that increasing the number of robots can directly improve task success rates.

  27. HuggingFace Daily Papers72

    OpenFlyV2 releases onboard vision-language-action model for aerial perception and navigation

    OpenFlyV2 is a unified onboard vision-language-action architecture that uses a shared vision-language representation to support semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within a single model. The model generates action chunks of 12 waypoints each entirely onboard at 10 Hz on an NVIDIA Jetson Orin NX, and transfers zero-shot from simulation to a real quadrotor, achieving a 48% navigation success rate in four real environments without using real aerial training data.

  28. Leiphone Robotics78

    Columbia's Yunzhu Li team presents FlexiTac open-source tactile sensor progress at IROS 2026

    At IROS 2026, Columbia University's Yunzhu Li team presented progress on FlexiTac, an open-source flexible piezoresistive tactile sensor platform, a year and a half after its open-source release. The sensor can be assembled in 3 minutes, bent and cut, and wrapped around gloves, fingertips, or robot skin. The team has integrated it into LeRobot and modified the UMI handheld device to add tactile recording, enabling synchronized visual-tactile data collection and policy pretraining. ADI is advancing a commercial version with tactile resolution five times that of a human finger, manufactured at scale using semiconductor fabrication processes.

    Why it matters: From sensor design and simulation calibration to UMI data collection, it presents a complete technical path toward scaling tactile perception.

  29. Leiphone Robotics62

    Xingyuan Intelligence Releases Cross-Embodiment Data Engine Chorus, Turning Human Manipulation Videos into Robot Training Data

    Xingyuan Intelligence has released Chorus, a cross-embodiment data engine for robot pretraining that can convert human manipulation data collected by devices such as smart glasses, chest-mounted cameras, wrist cameras, and UMI into training data adapted to different robot embodiments.

  30. Leiphone Robotics34

    Boyya Intelligence's Liu Xin: After GPT-6, hardware will become the bottleneck for robots

    Boyya Intelligence founder Liu Xin believes models may reach a capability inflection point earlier than hardware, at which point robots' bodies rather than their brains will limit their operation. Boyya's first dexterous hand, Gaoshan S1, has 22 degrees of freedom and uses a hybrid drive that is primarily tendon-driven, supplemented by direct gear drive and screw-rod linkages; the company says it is the world's first to use a tendon-driven plus screw-rod parallel wrist configuration. The team is also developing a "cerebellum" model with more than 30 built-in standard actions, which an upper-level model calls and adjusts execution details through real-time feedback.

  31. OFweek Robotics65

    Poke Robotics releases embodied API RoboAPI, paired with world-action model Poke-Aurora

    Poke Robotics has released RoboAPI, an embodied API for the real physical world. After connecting and adapting a robotic arm, developers can issue commands with a single prompt without collecting data or training models. Its self-developed world-action model Poke-Aurora, in the company's own large-scale evaluation, achieved success rates of 86.0%, 54.1%, and 87.7% on three task categories—object transfer and organization, pose and shape alignment, and environment interaction and tool use—all higher than GPT6 Astra and two open-source embodied models included in the comparison.

  32. OFweek Robotics58

    Unitree Technology Releases 2026 Third-Quarter Financial Report: Revenue 665 Million Yuan, Gross Margin Falls to About 50.6%

    Unitree Technology released its 2026 third-quarter financial report on October 29, with single-quarter revenue of 665 million yuan, up nearly 70% year on year; non-recurring net profit attributable to parent-company shareholders of 105 million yuan, down 17.4% year on year; and an estimated gross margin of about 50.6% for its main business, below the 56% in the first half. In the third quarter the company invested more than 127 million yuan in R&D, up nearly 250% year on year, and disclosed a plan to subscribe 20 million yuan for an equity stake in the Hangzhou Embodied Intelligence Application Pilot Base to maintain a 10% holding; the center has already collected and open-sourced its first batch of real-machine datasets based on Unitree robots.

  33. Reddit r/robotics36

    Local LLM converts English commands into general robotic arm control: Qwen3.8-Flash-Next drives reBot B601 demo

    A developer used a locally running Qwen3.8-Flash-Next model to convert English commands into general control of a reBot B601 robotic arm, entirely offline at home. In the demo, the arm completed tasks including shooting a Nerf gun, throwing a ball, writing, drawing, and pouring a cup, with object recognition using Meta SAM. This is the second video in the series; an earlier one demonstrated give, take, follow, and stacking actions.

  34. 雷科技78

    AgiBot Innovation Day releases Worken work-hour metric and Lingqu OS 1.0

    At AgiBot Innovation Day on October 29, AgiBot proposed Worken (effective intelligent work hours), a measurement standard whose formula is number of active robots × autonomous working hours × output efficiency ratio, and released Lingqu OS 1.0, a native operating system for embodied intelligence.

    Why it matters: At Innovation Day, AgiBot proposed the Worken work-hour metric and open-sourced Lingqu OS, giving readers a way to observe new paths for embodied intelligence commercialization and ecosystem openness.

  35. 量子位72

    Tsinghua University, Infinigence AI, and AgiBot propose collective experience learning CEL, validated on a 96-robot cluster

    Tsinghua University, Infinigence AI, and AgiBot jointly proposed the collective experience learning (CEL) paradigm, which lets robots share the successful experience of their peers rather than relying only on human demonstrations, and validated it on a cluster of 96 real robots. Two robots that did not participate in training were tested 600 times in total across five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—with 15 failures, for an overall success rate of 97.5%, all exceeding the average level of 6 human operators; the success rate on the phone packaging task rose from 79.2% to 98.3%.

  36. 智东西69

    Critical Point's dexterous hand completes 72-step industrial POC, 8.5 minutes per operation approaches human level

    At the Dexterous Manipulation sub-forum of AgiBot Innovation Day, Critical Point connected live to the Wolong Hill factory to present industrial POC results for a robotic arm plus dexterous hand. The system continuously completed a 72-step process including loading and unloading, barcode scanning, grasping a screw gun, tightening and removing screws, and pressing buttons. Phase-one task success rate reached 98%, with 8.5 minutes per operation, close to the roughly 8 minutes for a human, and it can run continuously for 8 to 12 hours.

  37. 36Kr Embodied Intelligence60

    Qiyuan Robotics launches PrimeSim world model and tops WorldSimProbe LIBERO leaderboard

    Qiyuan Robotics has launched PrimeSim, a world-model simulator for robot training, and topped the WorldSimProbe LIBERO leaderboard. PrimeSim builds an interactive, trial-and-error, continuously learning virtual world for robots, used for policy validation and reinforcement learning. In tests, reinforcement learning with it raised policy success rate from 61.7% to 73.2%.

  38. OFweek Robotics65

    Maxinsights (Haiyu Shuwei) completes $30 million seed round, betting on real-world robot training data collection

    Silicon Valley-based Chinese physical AI company Maxinsights (Chinese entity Haiyu Shuwei) announced the completion of a $30 million seed round led by Peak XV, with participation from Tola Capital, South Park Commons, Ince Capital, and Alumni Ventures. All funds will go toward real-world robot training data collection.

  39. OFweek Robotics82

    X-Era AI Tuoyuan Zhihui releases open-source 4D world action model E0.5, with out-of-distribution real-robot success rate surpassing π0.5

    X-Era AI Tuoyuan Zhihui released the open-source native 4D world action model E0.5, which uses a 3D foundation model as its backbone and integrates future 3D geometry prediction directly into action learning. On the AgileX Piper dual-arm robot and the Zerith H1 humanoid robot, after pretraining on about 160,000 robot trajectories, E0.5 achieved overall average success rates of 85.0% and 88.3%, respectively, improving on π0.5 by 13.3 and 26.7 percentage points; its out-of-distribution average success rates were 73.3% and 76.7%.

    Why it matters: The original text provides a comparison of E0.5 and π0.5 success rates on out-of-distribution real-robot tasks, which readers can use to judge the effect of 3D geometry supervision on manipulation generalization.

  40. HuggingFace Daily Papers75

    TinyAstral: Transferring a self-evolving harness to small generalist manipulation agents

    TinyAstral proposes a two-stage method: first, freeze an open-weight teacher model and use recursive self-improvement (RSI) to have frontier LLM agents read the teacher's trajectories and propose harness modifications, with each modification accepted only if it passes a paired statistical test; then freeze the final harness and distill a small student model.