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Only Opinion#Robot models and learning
Nov 7Sat
  1. 未来机器人网 · 微信公众号38

    Daxiao explores dual-brain collaboration: from real interaction to reusable physical experience

    Daxiao proposes a "dual-brain collaboration" architecture that connects the cloud brain SenseNova's task understanding and planning capabilities with the on-device Kaiwu world model's judgment of physical states and prediction of action consequences, while an embodied Harness organizes perception, memory, skills, and control. The exploration aims to preserve a single robot's interaction experience, along with evidence and applicable conditions, so that other robots can reuse it across tasks, scenarios, and embodiments, reducing repeated trial and error. The system is organized around three progressive loops: autonomous execution, experience learning, and recursive self-improvement. It is currently at the exploration stage and has not yet provided real-machine validation metrics.

Nov 5Thu
  1. 爱范儿34

    Interview with Stardust Intelligence's Lai Jie: the rope-driven robot, AOS operating system, and Lumo-2 embodied model's "iPhone 4 moment" has not yet arrived

    In an interview, Lai Jie, founder and CEO of Stardust Intelligence, said the company's architecture combines a rope-driven robot body, the embodied operating system AOS, and an embodied model. AOS packages perception, movement, manipulation, and interaction into skill modules, can adapt to large models and Agents such as GPT 6, and lets developers build applications using natural language. Its Lumo-2 embodied model has implicit world dynamics prediction capability and, combined with reinforcement learning, builds a trial-and-error and error-correction loop. Lai Jie believes the robotics industry's "iPhone 4 moment" has not yet arrived, but may be closer than imagined.

  2. 图灵人工智能 · 微信公众号34

    Building a Mind for Machines: Zhu Songchun, BIGAI's CUV Architecture, and the General-Purpose Intelligent Agent Tongtong

    At the 2026 World Artificial Intelligence Conference, Zhu Songchun reflected on the large-model approach and argued for replacing pure data fitting with BIGAI's CUV cognitive architecture, promoting the idea of "building a mind for machines." His team has released version 3.0 of its general-purpose intelligent agent Tongtong, upgrading its spatial, cognitive, and social intelligence, and it ranked first overall in eight categories of the team's self-developed General Social AI (GSA) test. This approach still faces three questions: evaluation independence, simulation-to-reality transfer, and a closed commercialization loop.

  3. 未来机器人网 · 微信公众号40

    Wang Xingxing at Wuzhen: 2027 likely the tipping point for embodied intelligence; humans' core value is setting goals and scoring for AI

    At the 2026 World Internet Conference Wuzhen Summit, Unitree founder Wang Xingxing judged that next year is likely the tipping point for the robotics industry or robot models, based on the rapid capability gains of foundation models such as GPT-6. He said the biggest current bottleneck is aligning models with real machines in the "last few millimeters" of precision assembly and flexible manipulation. Unitree is advancing a physical AI robot self-evolution system: AI agents autonomously search papers and open-source solutions to generate control code, which is validated in simulation and then deployed for real-machine testing, followed by closed-loop iteration combining automated evaluation and human scoring.

Nov 4Wed
  1. Robohub22

    AIhub coffee corner: Are humanoid robots the future of embodied intelligence?

    The AIhub coffee corner roundtable discussed whether humanoid robots represent the future of embodied intelligence. Participants noted that recent advances in hardware, algorithms, and training methods have driven leaps in capability, and that modern humanoid robots mostly use learning-based controllers and achieve walking through sim-to-real transfer.

  2. 图灵人工智能 · 微信公众号36

    Interview with Princeton's Liu Shilong: From Grounding DINO to self-evolving agents, humans are super routers

    Liu Shilong, a postdoctoral researcher at Princeton (joining Columbia University in fall 2027 as a tenure-track assistant professor), has shifted his research focus to self-evolving agents, with robotics and scientific discovery as application scenarios. He is a core author of the open-world detection model Grounding DINO, which has over 2 million monthly downloads on Hugging Face, and whose open-source project has accumulated over 30,000 GitHub stars.

Nov 3Tue
  2. 图灵人工智能 · 微信公众号40

    Tsinghua Professor Deng Zhidong: Physical AI Has Not Yet Had Its ChatGPT Moment; World Models Are Key

    Tsinghua University professor Deng Zhidong believes physical AI as a whole is still in an early exploratory stage and has not yet had its own ChatGPT moment; robots being able to run and dance does not mean they can enter homes to care for the elderly. He proposes that the key to a breakthrough lies in world models: letting AI rehearse consequences and costs before executing actions, combined with tasks and human intent, with evaluation criteria being generality, task success rate, and generalization ability. He also suggests connecting cloud, edge, and devices through mobile communications and deploying shared computing power at the edge to ease cost and power consumption pressures on device-side computing.

  3. 甲子光年 · 微信公众号38

    In conversation with Zhipu, Galbot, Guanglun Intelligence, and Deepexi: Will large language models eventually eat embodied models?

    At the "Qinghe Night Talk" roundtable, Zhipu CEO Zhang Peng, Galbot CFO Sun Dengke, Guanglun Intelligence's Yang Haibo, and Deepexi Technology's Zhao Jiehui discussed whether large language models will eat embodied models. Sun Dengke drew an analogy to fast and slow systems, arguing that embodied models handle S1/S0-level manipulation, planning, and environmental understanding, and may require a smaller S2 parameter scale than language models; Zhang Peng argued that intelligence will ultimately converge. Guanglun Intelligence positions itself as simulation-based physical AI data and evaluation infrastructure, while Deepexi Technology has launched DeepWorks, which can be tried for free.

Nov 2Mon
  1. 机器人产业应用 · 微信公众号58

    LLMs Enter Embodied AI: Competing for Physical Data or the Next-Generation Intelligent Gateway

    The article analyzes the different paths taken by large-model companies such as OpenAI, Google, Alibaba, and DeepSeek in entering embodied AI, arguing that their primary driver is their own growth and narrative needs, the second is defensive positioning, and that data and gateway are long-term goals not yet realized.

  2. Robotics & Automation News50

    Interview with TorqueAGI founder: Most robot AI is just a language or vision model with robot data added

    TorqueAGI is providing a "world reasoning model" intelligence layer for industrial robots and heavy machinery rather than developing its own hardware. The model represents objects, machines, and workflows as spatiotemporal graphs and incorporates physics and geometry into its reasoning. The company says new tasks typically require only dozens of examples rather than tens of thousands, and must be validated in simulation and in supervised operation at customer sites before unattended operation is possible. The same model has already run on single-arm and dual-arm robots, mobile platforms, scissor lifts, humanoid robots, and tractors, while details specific to each machine body still need to be described per machine.

Nov 1Sun
  1. OFweek Robotics36

    The stronger Astra gets, the more one question matters: Is a Chinese robot's "body" an asset or a peripheral?

    SenseTime's Astra reaches 100% on LIBERO-Long++ and 81.9% on RoboDojo; the stronger the general-purpose brain, the more easily a robot body that conforms to standard interfaces can be swapped out as a "peripheral." Using Unitree's real-machine data flywheel, Zhiyuan's 3/4 R&D investment in the large and small brains, and Galaxy General's AstraBrain-WBC 0.5 trained on 2 billion frames of motion-capture data (92.58% zero-shot motion-tracking success rate, 0.39 ms inference latency) as examples, the article proposes three criteria: depth of in-house development at the execution layer, data return rate, and scenario replacement cost.

Oct 31Sat
  1. 机器人产业应用 · 微信公众号58

    Representation, VLA, and World Models Struggle to Converge: Is a Unified Interface the Next Inflection Point for Robot Foundation Models?

    The article discusses the review paper "Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models," published on September 3, 2026 by institutions including Fujitsu and Carnegie Mellon University, and examines the integration challenges arising from the independent development of three research lines: representation learning, VLA, and world models.

Oct 30Fri
  1. 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.

Oct 28Wed
Oct 26Mon
  1. 人形大讲堂 · 微信公众号40

    Unitree's Wang Xingxing: The Tipping Point for Physical AI Is Within the Next Year or Two

    Speaking at the 2026 World Physical AI Conference, Wang Xingxing said that GPT-6 can directly control a robot to paint and autonomously wash brushes and change colors with zero training, and that robot planning and navigation problems have been largely solved; the remaining difficulties are the success rate of interaction with real objects and alignment with the physical world. He expects large models to solve assembly problems in one to three years, with voice-commanded room tidying achievable as soon as next year, and announced that the World Physical AI Competition will feature eight tracks and offer prizes of several tens of millions of yuan each year.

Oct 25Sun
  1. 3D视觉工坊 · 微信公众号22

    The hotter world models get, the more dangerous SLAM becomes? What robots lack isn't "imagining the future" but "remembering this moment"

    Addressing the problem that world-model predictions may be built on outdated perceptions of the environment, SLAM-MER at CVPR 2026 uses recent keyframe temporal information and a retrievable 3D representation for monocular SLAM, while GaME in the same year continuously updates its 3D Gaussian representation and removes outdated keyframe observations.

Oct 23Fri
  1. OFweek Robotics60

    Commentary: The stronger Astra gets, the more domestic robots must keep the execution layer in their own hands

    Using a real-world test report on SenseTime's Astra as its starting point, the article notes that Astra is outstanding at the planning layer (100% on LIBERO-Long++, 81.9% on RoboDojo), but real-machine operation exposes an architectural break between cloud inference's second-level latency and millisecond-level force-feedback closed loops; stacking bowls took an average of about 131 minutes and cost about 19 US dollars.

Oct 22Thu
Oct 21Wed
  1. LeadeRobot67

    Is the dexterous hand industry collectively cutting back?

    The article analyzes why the dexterous hand industry is collectively reducing degrees of freedom: manufacturers are now talking about lifespan, cycle time, and unit price rather than making hands more human-like. A new hand from a leading US robotics company has only four fingers and 13 degrees of freedom, while two new domestic products have 22 and 20 degrees of freedom, compared with benchmark products from just over a year ago at 38, 37, and 42.

Oct 20Tue
  1. 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.

Oct 19Mon
  1. OFweek Robotics62

    OpenAI announces in-house humanoid robot; author interprets its motives and impact on the industry

    Altman said OpenAI will develop its own humanoid robot, with a demo in 2027. The near-term goal is to assist skilled workers in building infrastructure, while the long-term goal is a personal robot. The author reviews OpenAI's two previous retreats: the 2019 robotic hand that solved a Rubik's Cube, the disbanding of its robotics team in 2020, and Figure's announcement in February 2025 that the partnership was ending. The author believes OpenAI now has the necessary conditions, including Aditya Ramesh's world simulation project, Astra's virtual capabilities, simulation infrastructure, and a teleoperation team of more than 100 people in San Francisco.

Oct 17Sat
  1. 钛媒体72

    General-purpose large models are redoing robot R&D and delivery

    Chen Youwen, an analyst at Zhixun Think Tank, compared two paths by which general-purpose large models are entering robotics: MicroFactory, in a single customer deployment, uses agents based on Claude Opus and GPT-6 Astra to generate control plans and dedicated programs for execution; according to the founder's own account, the robot autonomously assembled electronic products for 1.5 consecutive hours with zero operational errors throughout; Poke & Wiggle uses Claude Opus 5.5 to take over the entire process of reading papers, writing algorithms, training models, and testing on real robots, reproducing 10 recent papers.

  2. Reddit r/robotics49

    Team fully switches to frontier LLMs for robot control in customer deployments

    A team fully switched to using frontier LLMs to control robots in customer deployments over the past month. The architecture resembles a System 1/System 2 division of labor: high-level reasoning is handled by agents based on Opus and Astra, while the fast execution layer uses computer vision code written by the models themselves, which they describe as fast, editable, and reliable. LLMs also bring in-context learning and continual learning; when new situations arise, the LLM steps in and adjusts the process.

Oct 16Fri
  1. 高飞的电子替身 · 微信公众号63

    AlphaGo lead programmer David Silver: Abandon human data, achieve superintelligence within three years

    On the Sequoia Capital podcast Training Data, David Silver, lead programmer of AlphaGo and co-founder of Ineffable Intelligence, spoke with Lasse Espeholt. Silver gave two new predictions: strong evidence within one year that the superlearner approach is viable, and superintelligence within three years, more aggressive than his original estimate of three to five years.

  2. 机器之心 · 微信公众号69

    From Hardware and Data to Models: How Sharpa Systematically Advances Dexterous Manipulation

    In a conversation with Synced, Sharpa's academic lead Zhang Kaifeng outlined three main threads for advancing dexterous manipulation: human-centered data collection, high-frequency tactile low-level control that decouples motion from interaction, and tactile-anchored cross-embodiment representation learning.

  3. Reddit r/humanoidrobotics22

    Opinion: VLA models alone cannot achieve safe humanoid robot deployment

    A Reddit user argues that vision-language-action (VLA) models alone are not enough to safely integrate humanoid robots into daily life. Humanoid robots far exceed humans in strength and speed; without complex multi-sensory input, learning from video by vision alone cannot master the human instinct to adjust force at the right moment in tasks such as holding a child's arm, carrying a cake, or closing a door, which can easily cause accidents and hinder the industry's adoption.

  4. 亿欧网 · 微信公众号60

    Google, OpenAI, and DeepSeek Enter Together; Embodied AI CEO Says He Can't Sleep

    The article analyzes how, with the releases of GPT-6 Astra, Gemini 4 Argon, and the Pi API stacking up, the semantic, simulation, and fine-tuning layers of embodied intelligence are being taken over by general foundation model companies, and the world model is being demoted from overall commander to physical verifier.

  6. 未来机器人网 · 微信公众号55

    Wang Xingxing and Liang Wenfeng's moves point to an embodied intelligence inflection point next year

    The article analyzes three developments side by side and argues that the robotics industry may reach an inflection point next year: at a roundtable dialogue at the China International Industry Fair, Wang Xingxing said that, based on Unitree's deployment of GPT-6 to multiple robots for field testing, task completion exceeded expectations.

Oct 15Thu
  1. OFweek Robotics47

    Dexterous hands move past the DOF race: Boston Dynamics' new Atlas hand has four fingers and 13 degrees of freedom, with the VLA "brain" and cost emerging as bottlenecks

    Boston Dynamics has introduced a four-fingered dexterous hand for Atlas with 13 degrees of freedom in total, nearly double the previous generation's 7; Unitree's Dex5-S, released in September, also has only 22 degrees of freedom, as the industry shifts from a specs race toward pragmatism. Xinor Future says marginal returns diminish beyond 25 degrees of freedom, and existing VLA models can output only about 10 motion control vectors at the same timestamp, so many precise movements still require teleoperation. In the near term, low-DOF options such as three- and four-fingered hands remain mainstream, and products with 7-15 degrees of freedom are seen as the best market fit at this stage.

  2. OFweek Robotics69

    XPeng IRON set for Europe in 2027; media analyzes its strategy of mass-producing first, then finding scenarios

    Auto Pixel analyzes XPeng IRON's commercialization path: IRON plans to reach mass production by the end of 2026 and begin deliveries in the first half of 2027, entering the European market in the second half of 2027; the Pengxing robotics business has raised over $900 million, with a post-investment valuation exceeding $6.3 billion. The article notes that guided tours are the main initial scenario, and external customers need reasons to pay for redundant configurations such as dexterous hands and 2,250 TOPS of computing power, while store deployment is only the starting point of a data loop; the claim that about 70% of automotive technology reserves can be reused is hard-pressed to cover core barriers such as the brain and dexterous hands.

  3. 3D视觉工坊 · 微信公众号28

    SLAM's Biggest Challenge Isn't Localization—It's 'Remembering': How Robots Understand a Constantly Changing World

    SLAM's core challenge is shifting from precise localization to long-term "memory": robots need to continuously update their environmental memory and distinguish stable structures from dynamic objects. GaME and Flow4DGS-SLAM from CVPR 2026, and DyGS-SLAM from ICCV 2025, address scene changes from the perspectives of Gaussian representation, optical-flow assistance, and dynamic reconstruction, respectively.

Oct 13Tue
  1. OFweek Robotics68

    Wang Xingxing raises embodied intelligence forecast; Liang Wenfeng reassesses robotics strategy

    The article analyzes how Wang Xingxing, at the China International Industry Fair, shortened his most optimistic estimate for the tipping point of embodied intelligence from two to three years to one to two years, and admitted he had not believed in AI enough. Reports from Phoenix News Technology and others say Liang Wenfeng has been meeting intensively with embodied intelligence entrepreneurs; DeepSeek has already made a strategic investment in Unitree and plans to lead a funding round for Winverse AI, raising tens of millions of dollars at a post-money valuation of about 400 million dollars.

  3. OFweek Robotics58

    Unitree's Wang Xingxing Says He Underestimated AI's Ability to Build Robots Over the Past Decade

    At the 26th China International Industry Fair, Unitree founder Wang Xingxing said his biggest mistake over the past decade was not believing in AI enough. He said the release of GPT-6 greatly improved embodied intelligence practitioners' understanding, and that Unitree tested it on various robots with good results. He believes a breakthrough could come within one to two years if things move quickly, and that GPT-7 next year could directly solve the embodied intelligence problem.

Oct 12Mon
  1. Robohub54

    Humanoid robots can run and box, but folding clothes is the real test

    Wai-keung Fung of Cardiff Metropolitan University writes that at the World Humanoid Robot Games 2026, Tiangong Ultra completed the 100 meters in 8.64 seconds, faster than Usain Bolt's 9.58-second world record, but soft manipulation tasks such as folding clothes and doing laundry are a better test of robot intelligence. The article notes that fabric shapes vary widely and skills are hard to transfer directly between robots with different configurations and sensors, and that Open X-Embodiment is exploring this problem by gathering over a million real trajectories from 22 kinds of robots.

Oct 10Sat
Oct 9Fri
  1. Vulcan Robotics · X27

    Multimodal large models may replace robot-specific policies

    No training needed, no sim-to-real gap, just a prompt and a vision. Sentdex points out that every task in robotics today is a complex combination of locomotion, manipulation, vision, planning, and more, and fails once it deviates from training data or simulation characteristics. As a result, many demos are conducted only under perfect lighting and carefully placed objects, and never land under unknown real-world conditions—every deployment is a unique edge case. In the example, a general-purpose multimodal large language model (DSV4.1F) completed a robot task it was never specifically trained for, using an unseen camera, unlearned servo motors/motors, an environment it had never handled, and a morphology it had never been tested on. The video is at 10x speed and is currently slow, but this is already the slowest state at this level of intelligence. Sentdex says that the more he uses general-purpose multimodal large models, the more he realizes that specialized methods such as ACT and VLA may be mere supporting acts and distractions, and will eventually be swallowed by general models that do robotics incidentally. He does not think it will always be large language models, but the fact that large models can complete the most challenging robot tasks shows that... —Sourccey functionality is continuously improving.

Oct 2Fri
  1. Robohub31

    Robot Talk Episode 164: Accelerating Robot Learning with Michelle Lu

    Michelle Lu, co-founder and CEO of Vsim Technology, discusses using simulation and artificial intelligence to teach robots new skills on the podcast. She previously served as Director of Simulation Technology at NVIDIA, where she and her co-founder developed Isaac Gym, the first end-to-end GPU-accelerated reinforcement learning framework. In 2022, the two left NVIDIA to found Vsim, aiming to address limitations in efficiency, scalability, and the sim-to-real gap in simulation-based robot AI.