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All news · Today3 items

#Embodied AI

All collected and published items, ordered by time. Picks are a selected subset.
Opinion

Viewpoints, commentary, analysis, interviews, and discussion of trends.

Today10/17Sat
  1. 量子位43

    How VLA and World Models Give Robots an Embodied Brain

    The article describes how VLA models and world models are being developed and deployed in Shanghai: VLA enables robots to perform tasks, while world models predict what may happen before an action. Ant Lingbo’s “Lingbo General Brain” has been adapted to 17 robot brands and 20 configurations, and robots using it are working in multiple Shanghai pharmacy locations. The article says the two approaches are beginning to converge, while planning, standards, and evaluation are advancing.

  2. TechCrunch · AI39

    Nvidia Veteran Says Physical AI Is More Than Robots

    Amulya Vishwanath, a former NVIDIA robotics product marketing leader and founder of Techable VC, says robotics is in its “Lego era,” with basic building blocks still being rebuilt. On TechCrunch’s Equity podcast, she discusses investing in physical AI across robotics, materials science, and life sciences, and defines it broadly to include “a molecule, a material, a medicine,” or a factory machine.

  3. TechCrunch · AI32

    Former Nvidia robotics executive says the field is still in its “Lego era”

    Amulya Vishwanath, who spent seven years leading product marketing for NVIDIA’s robotics platforms, says robotics is still rebuilding basic components such as actuators, robotic hands, software, and safety standards. On TechCrunch’s Equity podcast, she discussed where she is investing through Techable VC, the early-stage firm she founded to back physical AI across robotics, materials science, and life sciences.

10/16Fri
  1. 量子位57

    Former Huawei Xiaoyi model lead Tang Duyu founds startup pursuing proactive physical AI

    Tang Duyu, former head of multimodal model development for Huawei’s Xiaoyi assistant, founded Guanxing Shuzhi to develop physical AI that identifies needs and decides when to intervene without explicit commands. Its ProAgent architecture pairs StreamingJev, a lightweight event monitor, with a Response Model that plans interventions and selects devices. The team reports testing guidance, warnings, and interaction on Reachy Mini across 60 cases, and introduced ProactiveXBench to evaluate intervention timing, progress, and outcomes.

  2. The Decoder75

    Google pitches Gemini 4 Argon for robot control, simulation, and training data evaluation

    Google is positioning Gemini 4 Argon as a single model for robotics simulation, control, and training data evaluation. Google reports that simulation-generated training episodes raised one robot’s success rate from 56 to 75 percent, but Argon ranked third in Artificial Analysis’s real robot control benchmark. The published demo prompts include extensive guidance, and Google does not report individual demo failure rates or the number of attempts behind the 75 percent result.

10/14Wed
  3. Latent Space73

    Dyna Robotics’ Jason Ma Explains Dyna-2 and Hour-Long Physical Agent Workflows

    Dyna Robotics co-founder and Chief Scientist Jason Ma explains how Dyna-2 combines future video prediction with robot actions and how agentic harnesses support hour-long workflows. He says the model was trained on over one million hours of human video, with some tasks requiring as little as 45 minutes of demonstrations. The interview covers napkin-folding deployments at Din Tai Fung, on-device inference, and hardware durability and missing sensory data as constraints on broader robot capabilities.

10/13Tue
10/12Mon
  1. 雷锋网38

    Former DeepRoute AI Executive Liu Nianqiu Says Mapless Driving Experience Can Transfer to Robots

    Femo One founder Liu Nianqiu says experience moving DeepRoute AI from high-definition maps to mapless driving can inform the company’s approach to robots, which he calls “Physical Agents.” Femo One is focused on open, dynamic environments and plans to begin deployments at multiple sites next year, building a cycle of deployment, data collection, training and redeployment.

10/10Sat
  1. 雷锋网28

    IROS 2026 keynote: more data does not mean better robot decisions

    At IROS 2026, University of Queensland researcher Jen Jen Chung argued in a keynote that data volume is not the same as information, and that blindly piling up data can degrade robot policies. Drawing on drone soaring, multi-robot communication and contact-rich assembly work, she said data value is unevenly distributed and that sharing or collecting only decision-changing information beats full broadcasts or uniform sampling.

  2. 雷锋网36

    IROS 2026 Signals Embodied AI's Shift From Concepts to Engineering

    At IROS 2026, running September 27 to October 1 in Pittsburgh, Leiphone reports embodied AI research is converging on four trends: foundation models internalizing physics, "brain-cerebellum" architectures becoming standard, touch as core to dexterous manipulation, and cheaper data collection. Papers cited include ULTRA, LITHE, VOFA, KineFuse, VTAP Gripper, ViHaTeleop, and HALO, alongside workshops on physical world models and uncertainty.

10/9Fri
  1. Latent Space36

    Periodic Labs' Liam Fedus and Ekin Dogus Cubuk on "synthesis superintelligence": from semiconductors to superconductors

    In an interview, Periodic Labs' Liam Fedus and Ekin Dogus Cubuk proposed a "synthesis superintelligence" approach: reinforcement learning in physical experiments, AI-driven materials characterization, density functional theory (DFT), and high-throughput labs. The aim is for models to learn the full process of doing science, rather than only studying published results.

10/8Thu
  3. MIT Technology Review · AI22

    AI advances in robotics will not change your life anytime soon

    An article produced by MIT Technology Review with Aventine says that while Elon Musk claims Tesla Optimus could eventually automate nearly all human labor for as little as $20,000 per robot and predicts public sales before the end of 2027, many robotics researchers are skeptical. Yann LeCun said at Davos that no humanoid robotics company knows how to make robots smart enough to be truly useful.

  4. 雷锋网22

    Li Yuan of Benewake: making eyes for machines, and eleven years of choosing the hard path

    Benewake founder Li Yuan has spent eleven years building lidar for drones, trains, cars, and robots. In the first half of this year, Benewake delivered 250,000 lidar units for robots; robotics accounted for more than half of company revenue, and 2025 robotics revenue doubled from 2024. Li believes measurements such as distance and position from lidar should become learning material that helps machines understand the physical world.

  5. MIT Technology Review · AI15

    How AVEVA is building a safer path to deploying industrial autonomous AI

    Arti Garg, AVEVA's chief technology expert, says industrial AI is shifting from predictive analytics to foundation models, physical AI, and agentic AI, and requires connected data, governance frameworks, and human oversight for safety. AVEVA's responsible AI framework emphasizes safety, efficiency, and human oversight, arguing that AI should augment rather than replace people in critical decisions. Garg also participates in an IEEE working group developing a standard method to measure AI's environmental impact across power, energy, resources, water, and carbon.

  6. 雷锋网32

    Dialogue with ONTIME Mobility COO Han Feng: The stronger the world model, the more real data becomes the true moat

    ONTIME Mobility COO Han Feng believes that as world model capabilities grow stronger, the importance of real data will not diminish: synthetic data is responsible for expanding known long-tail scenarios, while real data is responsible for discovering unknown problems and providing calibration and validation. ONTIME Mobility does not train large models; instead, it collects real data through routine mobility services, Robotaxi, and offline operations and maintenance networks, and delivers training materials to autonomous driving, world model, and embodied intelligence companies through a data closed loop.

10/7Wed
  1. Jim Fan · X27

    It is increasingly clear that humanity will solve the Riemann hypothesis before we pass the Physical Turing Test - you come back to a clean home after a wild Sunday party, and you can't tell whether a human or a robot did the job. We are not Moravec's paradox-pilled enough.

    It is increasingly clear that humanity will solve the Riemann hypothesis before we pass the Physical Turing Test - you come back to a clean home after a wild Sunday party, and you can't tell whether a human or a robot did the job. We are not Moravec's paradox-pilled enough.

9/30Wed
  2. 雷锋网28

    From 3D vision to world models: why spatial intelligence is a common thread in AI's move into the physical world | ECCV 2026 recap

    ECCV 2026 received 10,473 submissions and accepted 2,834. The 3D topic fell from a leading area to third place, but questions around 3D representation, geometry, and physical consistency are spreading into video generation, embodied AI, and world models. Of 93 workshops, about 10 were directly related to 3D, 11 to spatial intelligence, nine to physical AI, and five to world models. Best-paper and honorable-mention awards also focused on 3D surface representation, object reconstruction, and geometry perception.

  3. 雷锋网24

    Yang Sicheng of Yuansheng Intelligence: before embodied models improve, dexterous hands need to compete on performance

    Yuansheng Intelligence founder Yang Sicheng says that when models are not capable enough, hardware must be highly capable. Dexterous hands need to score above 80 on reliability, dexterity, payload, dynamic response, touch, and precision. Its in-house Apex Hand uses a hybrid in-palm tendon drive with 21 degrees of freedom, a maximum active payload of 30 kilograms per hand, and a 0.2-second open-close time. Teams using it won the gold, silver, and bronze heavyweight medals in weightlifting and tug-of-war at the 2026 World Humanoid Robot Games in August.

9/28Mon
8/20Thu
  1. Jim Fan · X54

    Jim Fan analyzes repetition, recovery, and UMI in GEN-1.5

    Jim Fan attributes GEN-1.5’s promise to naturally repetitive human demonstrations, retained failures that teach recovery, and UMI, which lets humans collect data directly while wearing a robot gripper. He argues that teleoperation loses physical intuition and that sufficient data can enable zero-shot behaviors such as picking up novel objects without finetuning. He remains cautiously optimistic, noting that in-context learning depends on the distance between training and test conditions and that current demos are too simple to draw conclusions.