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Only Opinion#Data and simulation
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. 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. 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. 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. Reddit r/robotics18

    Indian optical mocap studio seeks input: what human motion data is still missing for humanoid robot training?

    An Indian optical mocap studio offering body and finger capture and multi-person simultaneous capture is beginning to collect human motion data for humanoid robots, including ground transitions, stairs, and household chores. Before deciding on collection directions, the studio asked the community about gaps in public AMASS-type datasets regarding finger detail, object interaction, annotation, and capture scope.

  3. 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. Robotics & Automation News40

    BlueBotics CEO: The highest-risk automation decisions happen before deployment

    BlueBotics CEO Nicola Tomatis points out that key decisions in mobile robot projects—such as fleet size, routes, charging points, and pick/drop locations—are already fixed before deployment, and simulation can verify these assumptions in advance. In simulation tests, his team found that after accurately modeling pick/drop behavior, similar vehicle configurations required about 30% different numbers of vehicles to complete the same task; adding just 1 second of delay at each intersection also significantly affected system performance. Once a project reaches about 5 to 10 vehicles, the value of simulation rises rapidly.

  2. Reddit r/humanoidrobotics18

    Can virtual worlds let AI learn to help robots before entering the real world? A discussion on Google Genie 4 and robot training

    A discussion proposes that robots could first practice obstacle avoidance and grasping different objects in virtual environments before working in real warehouses, using Google Genie 4's interactive world model as a potential research tool. Genie 4 is not a robot control system, and whether interactive world models can play a greater role in robot training remains to be explored.

Oct 29Thu
  1. Xbotics具身智能实验室 · 微信公众号38

    From working hours and human intervention rate to human efficiency ratio: How far are robots from becoming a productive force?

    RealMan Intelligent is advancing the real-world deployment of robots through three metrics: working hours, human intervention rate, and human efficiency ratio. Its integrated joint modules have passed CR L3-level reliability certification and achieve a mean time between failures of 50,000 hours, and nearly 100 robots are already providing continuous service in industrial and commercial scenarios.

Oct 28Wed
Oct 21Wed
  1. Reddit r/robotics12

    Can university lab data be used to train robot policies? A robotics newcomer asks on Reddit

    A Reddit user who recently graduated from a health-focused university and has just entered the robotics field asks whether video, motion, eye-tracking, depth-sensing, and expert-annotated data that sit idle in university labs after papers are published could be used to train robot policies, for example for specialized procedures such as surgery. The poster is looking to connect with practitioners working on robotics data pipelines (collection, cleaning, annotation, training).

Oct 16Fri
  1. 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. OFweek Robotics63

    CIIF roundtable discusses three-layer data pyramid for humanoid robots: how human, simulation, and real-machine data build on one another

    At a roundtable discussion on October 12 at the 26th China International Industry Fair, Sharpa co-founder Xiang Shaoqing proposed a data pyramid framework in which human operation data, simulation data, and real-machine data build on one another. Unitree founder Wang Xingxing responded that he is more focused on the last few millimeters of manipulation precision in real-machine data, and Academician Wang Jian called for opening up industrial data. The article reviews the differences between Figure Index crowdsourcing, data collection at Tesla's Optimus Academy factory, and China's precision-first approach, and notes that the key dividing line is who first completes the full data loop from collection to use.

Oct 15Thu
  1. 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.

Oct 14Wed
  1. Reddit r/robotics43

    How do you determine whether teleoperation or human video data is of sufficient quality for training? A discussion on data quality becoming a bottleneck

    Pantheon's dataset audit released last month found issues in several popular open-source robotics datasets, including frozen or disconnected wrist cameras, gripper actions recorded about two frames earlier than the actual motion, recording stopping mid-action, and long idle tails after task completion; the RoboDrop paper (arXiv 2609.10021) also points out that execution errors, sensor drift, and timestamp misalignment are common errors.

Oct 13Tue
  1. Reddit r/humanoidrobotics45

    Opinion: We may be underestimating the role of the camera itself in robot datasets

    Drawing on experience with first-person robot data processing, the author points out that storing only RGB frames makes observations hard to localize in 3D space; camera intrinsics, distortion parameters, and stereo extrinsics, combined with matching algorithms, can recover depth in real units downstream. In its egocentric-view collection setup, the team synchronously records calibrated stereo video, camera poses, body skeleton, and hand coordinates, and combines this with motion tracking to support downstream tasks such as reconstruction, multi-sensor alignment, and cross-embodiment transfer. The author stresses that sensors and dataset design should be planned together, since calibration and synchronization errors can make the lowest-cost datasets hard to use.

Oct 10Sat
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.

Sep 30Wed
  1. Leiphone Robotics64

    Yuansheng Intelligence's Yang Sicheng: Before Embodied Model Capabilities Break Through, Dexterous Hands Must First Compete on Performance

    In an interview, Yuansheng Intelligence founder Yang Sicheng proposed that when model capabilities are not strong enough, hardware capabilities must be very strong, and only after models break through can dexterous hands simplify. Its self-developed Apex Hand uses an in-palm hybrid tendon-driven scheme, has 21 degrees of freedom, a maximum single-hand active payload of 30 kg, and an open-close time of about 0.2 seconds, and was carried by the gold, silver, and bronze medal teams in the heavyweight categories of weightlifting and tug-of-war at the 2nd World Humanoid Robot Games in August 2026. He emphasized prioritizing reliability and dexterity, using unidirectional tendon drive plus end encoders to compensate for creep, and believes the endgame for dexterous hands is a reliable general-purpose productivity tool.

Sep 19Sat
Sep 14Mon
Sep 10Thu
  1. 云启资本60

    Yunqi Podcast Roundtable: Three Embodied AI Founders Discuss World Models, Scaling, and Deployment Bottlenecks

    During the World Robot Conference, Yunqi Capital co-hosted a themed roundtable with SEE Fund, which was included in the podcast Attent!on. Three founders—Xu Huazhe of PokeBot, Li Qiang of Quantum Dynamics, and Liu Nianqiu of Feimo Yi Technology—shared their views on world models, scaling, and deployment.

    Why it matters: The three entrepreneurs approached the topic from models, hardware systems, and mobility respectively, specifically breaking down where the bottlenecks in embodied intelligence deployment lie and where they diverge on scaling efficiency.

Aug 26Wed
  1. OFweek Robotics52

    Leju Hosts Three Dialogues at WRC: Robotics Industry Shifts from Making Movements to Doing Things Reliably

    The author reviews three dialogues hosted jointly by Leju and Ant Lingbo at WRC, covering whether embodiments are easy to learn, whether data is easy to use, and whether models are easy to evaluate, with 12 guests in total. Key viewpoints include that hardware is becoming a precondition for model capability, that Ant Lingbo's real-world data exceeds 100,000 hours and covers more than 20 configurations, that Noitom says about 20% of data plays a core role in model behavior, and that evaluation should guide adjustments to data distribution; on this basis, the author argues that industry attention is shifting from making a single movement to doing a task reliably and consistently.

Jul 13Mon
Jun 30Tue
Jun 15Mon
  1. 石麻笔记 · 微信公众号67

    In Conversation with Jianyuan Wang, First Author of VGGT: The Future of 3D Reconstruction Foundation Models and Robotic Spatial Perception

    This article is an interview with Dr. Jianyuan Wang, first author of VGGT, which won the CVPR 2025 Best Paper Award, and of VGGT-Ω, a CVPR 2026 Best Paper finalist. He is currently pursuing a PhD through a joint program between the Oxford Visual Geometry Group and Meta AI.

  2. New Scientist Robots56

    Reporter visits Physical Intelligence: progress and controversy on the general-purpose robotics path

    Physical Intelligence is developing a general-purpose control brain that can work across many kinds of robots, using a vision-language-action (VLA) model to teach robots tasks such as making coffee, folding clothes, and peeling vegetables, rather than focusing on a single machine as Tesla, Boston Dynamics, or Amazon do.

Jun 4Thu
Feb 2Mon
  1. 线性资本62

    Interview with Tashi Intelligence's Chen Yilun: No VLA, no simulation—an embodied AI company's contrarian bets

    Linear Capital reposted LatePost's interview with Tashi Intelligence founder Chen Yilun, who laid out a technical path that differs from the mainstream: he rejects the VLA architecture that grows an action head on top of a VLM, and instead develops its own AWE (AI World Engine), aiming to represent physical quantities such as time, space, and force; it has also abandoned teleoperation data collection in favor of a self-developed collection device combining a wearable glove and a first-person-view camera.

    Why it matters: The interview systematically presents technical judgments and a data approach that differ from the mainstream VLA path, giving readers a basis for understanding the specific grounds of the divergence in embodied AI approaches.

Jan 12Mon
  1. Skild AI48

    Skild AI: Enabling robots to learn new skills by watching human videos

    Skild AI proposes overcoming the robot data bottleneck by watching human videos, arguing that teleoperation is limited by environment diversity and real-time collection scale and cannot reach the data volume required by foundation models. Its approach is cross-embodiment learning: with less than 1 hour of robot data for fine-tuning, the model can learn new skills from video demonstrations alone, mapping human "grasping" onto the execution actions of different robot embodiments.

Dec 23Tue
  1. RealMan/睿尔曼27

    Realman Founder Zheng Suibing Proposes Building a Remote Workforce Network at Corporate Social Responsibility Forum to Create a Sustainable "Real-Machine Data Engine"

    Zheng Suibing, founder and CEO of Realman, said on December 19 at the 2025 Corporate Social Responsibility Forum that the company is using a "remote workforce network" to let operators direct robots in real time to perform real tasks such as folding towels, utility tunnel inspections, and power control, while accumulating scenario data to feed embodied intelligence. Realman's integrated joint modules have annual production capacity exceeding 100,000 units, its robotic arms have passed CR L3 certification, and their mean time between failures has reached 50,000 hours. The company also co-founded the GPR open-source alliance in February 2023 and released a mobile composite robot development platform in 2024.