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Nov 14Sat
  1. The Decoder60

    OpenAI reassigns robotics researchers to other teams as general-purpose models might make specialized robotics AI unnecessary

    OpenAI is disbanding its robotics team again, moving robotics researchers to Mark Chen's main research team and the applied robotics and hardware teams to Greg Brockman's Applied organization, according to Fortune. The company reportedly believes general-purpose models like GPT-6 Astra can control robots directly, citing an Nvidia study where an Astra-powered agent reached a 74.8 percent success rate on simulated robotics tasks versus 51 percent for the best specialized models, and a StationeryBench comparison where Astra fully completed 7 of 100 tasks while Ai2's MolmoAct2 completed none. OpenAI shut down its robotics division in 2020 and rebuilt it in 2024, with CEO Sam Altman reportedly targeting a first public robot demo for 2027.

  2. The Decoder65

    Runway's open-source Praxis-1 turns video AI into a robot controller

    Runway has released Praxis-1, a World Action Model that takes camera images, text instructions and robot position data to predict scene changes and derive robot movements, with weights and code under the Apache 2.0 license. Built on a video model fine-tuned with human action videos and robot data, it scored 46.2 percent on Nvidia's RoboLab-120 leaderboard, ahead of FLUX 3 Action at 42.9 percent, though Runway ran the tests itself and they have not been independently verified. The model still needs specialized robot data for adaptation, sometimes confuses similar-looking objects, and cannot reliably plan complex action sequences on its own.

  3. Hugging Face Blog34

    Build a Robot Pick-and-Place Simulation with SimReady Assets

    This walkthrough shows how to assemble a UR17G arm, Robotiq 2F-140 gripper, and beverage can from SimReady Central in Isaac Sim 6.2, then use an agent to generate and run a pick-and-place task. It explains SimReady profiles, features, and requirements, and lists checks for joint settings, movement, and contact before running the grasp, lift, transfer, and release sequence. The example uses a scripted horizontal grasp with a 2 N·m drive-effort cap and fixed waypoints, and the authors note the results do not establish robustness to uncertain object positions, different payloads, or real hardware.

  4. NVIDIA Technical Blog31

    Adapt Human Motion for Robots with NVIDIA SOMA Retargeter

    Human motion can serve as a starting point for teaching humanoid robots to move, since human demonstrations capture the coordination and timing behind natural movement. However, a robot cannot simply copy the same joint motion from a human because its proportions, joint structure, and range of motion differ, so the movement must be adapted to fit. NVIDIA's SOMA Retargeter is presented as a tool for this adaptation.

Nov 13Fri
  1. MIT News AI22

    Helping robots fit into the human workplace

    Robust AI, co-founded by MIT Professor Emeritus Rodney Brooks and Anthony Jules, builds a mobile robot called Carter that is designed to work alongside humans in warehouses and factories, featuring a force-sensitive handlebar, an AI-powered vision system, and holonomic wheels. Carter is deployed in logistics warehouses and on factory floors to help workers pick or stock shelves and move items, with workflows that let it guide workers to locations, follow them, or transport loads across large facilities. The company says its approach is to understand workers' primary tasks and build features that accelerate them, letting humans do what they are good at while digital systems handle the rest.

  2. Hacker News AI12

    The World Builder the AI Labs Haven't Called Yet

    The article asks why Eiichiro Oda, the creator of One Piece, is not consulted by frontier AI labs building world models, arguing that his expertise in macro-level geography and persistent thematic continuity differs from the low-level physics and spatial consistency such models require. It suggests his value would lie in gaming, generative media and simulation styling, where internal logic continuity matters, while noting the odds of recruiting him are low given his commitment to One Piece.

Nov 12Thu
  1. Hacker News AI38

    Laser-armed AI submarine robots patrol Norwegian salmon farms, killing sea lice

    Stingray Marine Solutions' latest annual report says its AI laser-equipped submarine robots have been a success for Norwegian salmon farmers, with over 3,500 units deployed around salmon pens along the Norwegian coast protecting over 112 million fish. Deployments are up by about 1,000 over the last year, making the operation one of the largest commercial AI deployments in Scandinavia.

Nov 11Wed
  3. Ars Technica · AI38

    Ring cameras go mobile as Amazon debuts AI-powered security drone and robot

    Amazon introduced the Ring Drone Cam, which offers a 148-degree field of view, 360-degree rotation, 2K video and low-light LEDs, but is limited to about 8 minutes of flight time on a new, fully charged battery and may perform worse near reflective, featureless or transparent surfaces. Amazon also pitched the $1,000 Ring Rover Cam, an autonomous indoor security robot with up to 4K livestreaming and recording, cloud storage of events for up to 180 days, and preprogrammed patrol points, available for preorder with shipping set for December 16, 2026. The wheeled Rover is limited to single-floor indoor use and is not compatible with curved edges, sunken areas, flooring transitions over 1.375 inches or black glossy floors.

  4. The Decoder72

    Nvidia's model GR00T 2 watches humans on video, then imagines itself as the robot

    Nvidia's GR00T 2 is a "world-action model" that predicts both the next movement and a short video of the near future, letting it transfer human movement sequences from video to different robot form factors instead of relying on teleoperation. Trained on over a thousand GB300 chips with about 400,000 hours of head-mounted-camera footage, it learned tasks such as lighting a match or slicing a banana from human demonstrations, with success rates between 10 and 80 percent, and a higher-level language model raised toy-car assembly success to at least 97 percent. Nvidia notes hurdles including no direct touch sensor data, missed subtle errors, and idle time of roughly a third during trash sorting while waiting on the planning model.

  5. TechCrunch · AI58

    Ring, known for its doorbell cameras, launches a home security drone and an enterprise security robot

    Amazon-owned Ring announced Ring Robotics and two devices: the Ring Drone Cam, a 2K "flying indoor security camera" that patrols the home and can be sent to investigate alarms, and the Ring Rover Cam, a 4K enterprise robot with a periscope camera and two-way audio that patrols set routes. Both use Ring's AI Pilot system to map spaces autonomously and record video protected by TAKE encryption. The Rover launches December 16 at $1,000, and the drone follows on January 27 at $270.

  6. The Decoder77

    Google DeepMind's Gemini Robotics 2.5 scores 96 percent sorting metal parts in a Mercedes-Benz assembly pilot

    Google DeepMind introduced Gemini Robotics 2.5, a vision-language-action model that converts visual input and language instructions into robot movements, reporting 30 to 100 percent higher success rates and 20 to 30 percent faster task completion than its own baselines across more than 200 tasks. In a pilot with Apptronik and Mercedes-Benz on active production lines in Kecskemét, Hungary, and Sindelfingen, Germany, the Apollo 2 humanoid sorted metal parts from bulk bins into trays, achieving a reported 96 percent success rate. DeepMind also published the open-source ASIMOV-Jailbreak benchmark on Hugging Face and said safe task completion improved by 33 percent near obstacles and 43 percent near people.

    Why it matters: It documents a concrete humanoid-robot deployment on live automotive lines alongside a new safety benchmark, useful for tracking how VLA models move from demos to production.

Nov 10Tue
  1. Ars Technica · AI71

    DeepMind reveals Gemini Robotics 2.5 and open safety benchmark "ASIMOV-jailbreak"

    Google DeepMind unveiled Gemini Robotics 2.5, a vision-language-action model it says is much better at high-dexterity tasks, evaluated across more than 200 activities including serving food with tongs and using a pipette. The platform pairs Gemini Embodied Reasoning, which plans tasks from instructions and live video, with the VLA that generates robot actions; some Gemini robots did actual work in Mercedes-Benz factories. Google also showed Apptronik's Apollo 2 humanoid with Gemini Robotics 2.5 tying a bow with its five-fingered hands, and the team is preparing an open source safety benchmark for robots working alongside humans.

  2. Google DeepMind78

    Gemini Robotics 2.5 accelerates real-world general dexterity

    Google DeepMind introduced Gemini Robotics 2.5, its most capable vision-language-action model, which it says raises skill breadth and learning speed, with 30% to 100% higher success rates and 20% to 30% faster task completion than previous baselines across 200 tasks. In a proof-of-concept on Mercedes-Benz production lines in Hungary and Germany, Apptronik's Apollo 2 humanoid performed automotive kitting with a 96% success rate. The release also adds inference-time safety steering (33% better safe completion around obstacles, 43% around people) and the open-source ASIMOV-Jailbreak benchmark, on which Gemini 4 Argon reaches 99.5% jailbreak resistance.

    Why it matters: It offers a concrete look at how frontier VLA models are being measured and piloted on real factory work, including reliability and safety benchmarks.

  3. The Verge · AI58

    Hands-on with Ring’s new AI drone camera for your home

    Ring says its Drone Cam, an AI-piloted indoor drone that flies pre-programmed routes to livestream and record when triggered by Ring sensors, will ship on January 27th, 2027, for $249.99. The Verge's hands-on found a 165-gram device with 2K resolution and a 148-degree field of view that relies on cloud-based AI Pilot for navigation, offers 5 to 8 minutes of flight per charge, and requires about 8 minutes of charging per minute of flight. Ring also announced the $999.99 Ring Rover Cam, and said the Drone Cam is still obtaining FCC certification.

  4. Hacker News AI55

    Xinhua Unveils Bionic Humanoid Robot News Anchors with Ubtech – Unite.ai

    Xinhua News Agency unveiled two bionic humanoid robot news anchors, Xinyu and Xinyan, developed with UBTECH and presented on November 5, 2026, at the Artificial Intelligence and Chinese Modernization forum in Beijing, which Xinhua describes as the world's first of their kind. The robots connect to Xinhua Yudian, the agency's AI current-affairs information agent and authoritative corpus, and UBTECH supplied bionic skin, embodied-intelligence hardware, an operating system, an emotional large model and full-robot manufacturing. Xinhua's Qian Tong framed the robots as a new editorial role supporting journalists with information presentation, multilingual output and live interaction, while planned uses include research and study bases, city reception venues, overseas cultural exchanges and deep-space exploration reporting.

  5. NVIDIA Technical Blog22

    Make a Robot Arm Feel Weightless with NVIDIA Holoscan 5

    NVIDIA describes using Holoscan 5 to build a gravity-compensation loop that lets a robot arm be guided by hand, with its motors supplying just enough torque to support its weight and keep it upright. The post says this interaction is a useful starting point for robots that learn by demonstration, manipulate objects, or work alongside people, and that such a loop requires continuously calculating the inverse dynamics.

  7. Hacker News AI71

    AI agents use simulation to solve tough robotics problems with code, not data

    At the 2026 Conference on Robot Learning, UC Berkeley and NVIDIA announced "agentic robotics," an approach in which LLM-based coding agents decompose tasks like grocery packing into sub-tasks, write and iterate code in simulation, and even tune simulation parameters to match real-world video. UC Berkeley testing reached success rates up to 99 percent, matching human hand-engineered performance, versus 20 percent for baseline imitation learning, and NVIDIA's Spencer Huang said retasking a robot at Wistron drops from at least 8 hours to a minute or two. Intrinsic's Torsten Kroeger cautioned that no physics engine accurately models physical contact and raised alignment concerns about trusting agents.

Nov 9Mon
  1. NVIDIA Newsroom78

    Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation

    At CoRL in Austin, NVIDIA introduced open robotics technologies including GR00T 2, an open world action model built on NVIDIA Cosmos and trained on diverse robot and human data including 100,000 hours of open source human egocentric data, plus Isaac Lab 3.0 with tighter Newton integration for deformable objects and a new reinforcement post-training library. Two NVIDIA papers, GRAIL and RoboTTT, are among six finalists for Best Paper Awards, and partners including Ambi Robotics, Bosch Research, Siemens, Skild AI and UC Berkeley are applying the tools to benchmarks such as DexBench and VABAR and to agentic workflows like Graph-as-Policy. The piece also details AlphaNIST, which trains a single policy across all 20 NIST Assembly Task Board tasks with over 98% training success using roughly 39x less GPU time, and Isaac Lab 3.0 with Newton reaching up to 2.5x the training throughput of Isaac Lab 2.3 with PhysX in a vision-based dexterous manipulation benchmark.

    Why it matters: It offers a detailed, source-grounded survey of NVIDIA's CoRL announcements and partner deployments, useful for readers tracking simulation-based robot learning and evaluation benchmarks.

  2. Import AI48

    Import AI 480: Self-sustaining space economies; AI for science progress, and Meta builds perfect hands for a superintelligence

    This Import AI issue covers Physical Superintelligence's research system, running a non-public Anthropic model, finding a Lean-verified proof of the Aaronson-Ambainis quantum conjecture; Meta Reality Labs' Tactus textile-integrated tactile and pose sensing glove for manipulation data collection; a 2016 NASA paper on bootstrapping a self-sustaining lunar industry with 12-41 metric tons of launched mass; and Core Automation's distributed training of a roughly 32B-parameter mixture-of-experts model across clusters 9,000 miles apart.

  3. MIT News AI38

    AI-assisted learning platform helps robots reason beyond literal commands

    MIT and NYU researchers introduced FLEX, a flexible feature-editing pipeline for robot motion-planning that lets robots interpret natural-language commands and generalize beyond their literal meaning. FLEX uses a three-step process — Add, Generalize (via the LLM GPT-4o), and Mask — so a robot told "Don't spill on the laptop!" also avoids other electronics, and it was demonstrated in simulations of serving coffee, moving a candle, and carrying a bowling ball. The study, led by MIT assistant professor Andreea Bobu, will be presented at the annual Conference on Robot Learning.

  4. NVIDIA Newsroom36

    Korea Advances Physical AI Across Industries With NVIDIA

    NVIDIA is helping advance Korea's physical AI ecosystem, with industry leaders applying accelerated computing, AI and simulation across semiconductor manufacturing, robotics and autonomous vehicles. Samsung Electronics is expanding its NVIDIA collaboration in semiconductor engineering, including a case where AI homogenization with coarser grids and GPU acceleration cut simulation time from about 18 hours on a 32-core CPU to 41 seconds. Kakao Mobility plans to build a level 4 autonomous driving system using NVIDIA Hyperion and Halos, while LG Electronics demonstrated NVIDIA Isaac GR00T-powered robotic manipulation with its CLOiD robot in a manufacturing environment.

Nov 7Sat
  1. Hacker News AI58

    VFlash: Enabling System-1 VLMs for Physical AI

    VFlash is a generalist decoding framework for Physical AI that adapts VLM speculative decoding by exploiting the known structure of visual primitives, using a lightweight drafter trained on annotated geometry to generate complete primitives in one parallel pass while the target VLM stays frozen. It reduces mean 3D displacement error by 42.6% relative to the autoregressive target across three robot datasets and three target variants, and reaches 70.2% average success on MolmoSpaces versus 62.6% for the baseline. On Jetson Thor, DGX Spark, and H100, VFlash cuts decoding latency by 12–42×, yielding 5× faster robot inference and over 3× faster NAVSIM driving inference on Jetson Thor and DGX Spark.

  2. The Decoder60

    Frontier Radar #5: If AGI is so close, why is everyone leaving? The neolabs and their bets

    This newsletter surveys 27 independently founded, research-driven "neolabs" in the US and Europe, grouped by their primary technical bet, and contrasts their founders' doubts about today's recipe of ever-larger language models with claims from Jensen Huang and Greg Brockman that AGI has arrived. The first group targets continual learning and learning from few examples (Adaption, Core Automation, Flapping Airplanes, Ineffable Intelligence, Ndea, Safe Superintelligence), while the second bets language alone is insufficient and pursues spatial and physical intelligence (AMI, Black Forest Labs, General Intuition, Physical Intelligence, World Labs). It notes the funding behind these labs, including Nvidia's reported five billion dollar investment in SSI and AMD's planned acquisition of World Labs for about 8.2 billion dollars, and asks whether they can deliver something the big players lack.

Nov 6Fri
  1. Allen Institute for AI Blog55

    ToyBlocks: A controlled world for robot learning and evaluation

    Ai2 introduces FoundationWorlds, a framework pairing controlled simulated environments with tools for automatically generating tasks, demonstrations, and behavioral metrics, starting with ToyBlocks, an open simulation of a robot arm manipulating blocks on a tabletop. ToyBlocks generates demonstrations via reinforcement learning, motion planning, and a task-and-motion planner at up to 24 times real-time on a single GPU, and supports studies of data scaling, spatial generalization, long-horizon planning, world models, and sim-to-real transfer. The initial release includes 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames, plus two trained models, training code, and evaluation code and validation sets.

  2. Hacker News AI58

    Physical AI can't learn from the internet. We build the worlds it learns from

    Physicl launched Studio, a beta app that lets researchers describe a failure case in plain words and then proposes a matching scene from a library of 10,000 environments and 100,000 assets, suggesting variations to cover it. It renders images and video with ground truth computed from the 3D scene on every frame, including depth, surface normals, segmentation, boxes and camera pose. Studio is free during the beta and opening to research teams in waves.

  3. Hugging Face Blog58

    ToyBlocks: A controlled world for robot learning and evaluation

    ToyBlocks is an open simulation framework built around a robot arm and blocks on a tabletop that automatically generates tasks and demonstrations, using reinforcement learning and motion planning to produce skills such as pushing, flipping, and stacking. It supports studies of low-level action model generalization, high-level planning with vision-language models, agentic systems using tool calls, action-conditioned world models, and zero-shot sim-to-real transfer, with an initial release of 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. The authors present ToyBlocks as the first of their FoundationWorlds and plan a second release with a larger dataset, more skills, and integrated RL training.

  4. Hacker News AI58

    Tesla to wind down Cybertruck production as Elon Musk focuses on robotics and AI

    Elon Musk said Tesla will stop Cybertruck production at its Texas factory around the middle of next year to make room for increased Cybercab output, without saying whether the vehicle will be built elsewhere. The Cybertruck, which began deliveries in late 2023 and was never sold in Europe for failing to meet safety standards, fell far short of Musk's 200,000-a-year target, with Tesla delivering only 8,300 "other models" in the third quarter of 2026. Musk has pinned Tesla's hopes on robotics, AI and an autonomous driving fleet, and the Texas plant will also build the Optimus humanoid robot.

  5. TechCrunch · AI38

    Physicl raises $10M to make the digital world real enough for robots

    Physicl, founded by Alex de Vigan, has raised $10 million in seed funding from Inovia Capital to build more realistic simulations for training physical AI and robot models. The startup is releasing a product called Studio with 10,000 3D environments and 100,000 assets that lets users create scenes to generate synthetic training data for difficult edge cases. World model builder SpAItial collaborated with Physicl as an early customer to improve the fidelity of its products.

  6. MIT Technology Review · AI38

    I watched a humanoid robot clean my apartment. It felt like a taste of the future.

    A reporter signed up for Tau Robotics' humanoid-robot cleaning service in San Francisco, where the four-and-a-half-foot robots are currently piloted remotely by humans who watch via cameras in the robots' heads and wrists, collecting training data for future autonomy. The first visit ended early when heat disabled the human controller's equipment, but a second cleaning went more smoothly, with the robot handling the kitchen and toilet despite mishaps like leaving taps dripping and toppling an air purifier. Tau cofounder Cornelia Weinzierl says the robots can already vacuum and mop without human help, and the company has completed only a couple hundred cleanings so far.

  7. MIT News AI34

    Clearing up a robot’s fuzzy memory

    MIT researchers developed UQ-DAAAM, a memory system that quantifies a robot's uncertainty about object descriptions by comparing multiple viewpoints and fusing them into more accurate captions, resolving ambiguities for about 92 percent of objects with only two additional captions each. The system improved object description accuracy by 5 percent over the state of the art and produced more accurate answers about objects, their locations, and when they were seen. The work, led by senior author Luca Carlone with co-lead authors Harry Zhang and Nicolas Gorlo, will be presented at the Conference on Neural Information Processing Systems.

  8. Hugging Face Blog62

    New NVIDIA Cosmos 3 Open Models for Interactive World Simulation

    NVIDIA's Cosmos world model platform adds four new open-weight checkpoints to its Cosmos 3 family: Cosmos3-Nano-Transfer-Auto for multi-view driving data with varied weather, lighting and time of day; Cosmos3-Nano-Sim-Auto for streaming synchronized camera and LiDAR driving simulation; Cosmos3-Nano-Sim-Transfer for turning simulator controls and text prompts into photorealistic video; and Cosmos3-Nano-Sim-Bimanual for predicting video from robot or camera actions. All four are post-trained examples of Cosmos 3 Nano with 16B trainable parameters and use OpenMDW1.1, permitting commercial and non-commercial use under its terms. The post also details a four-stage training recipe (bidirectional adaptation, teacher forcing, consistency distillation, self-forcing) and post-training guidance for adapting the models to new sensor rigs and embodiments.

    Why it matters: It gives a concrete inventory of newly released open-weight world-model checkpoints plus the training recipe, useful for researchers weighing them for robotics or driving simulation.

  9. Hacker News AI40

    AI e-nose and wearable device deliver programmable smells with 73% accuracy

    Researchers at The Ohio State University developed an AI e-nose paired with a wearable scent-release system called Scentronic, which uses machine learning to classify odor data in real time. Trained on five food scents (coffee, beer, red wine, orange and chocolate cake), the system let participants identify individual odors about 73% of the time and paired scents about 71% of the time. The prototype was built in wristband and necklace versions, and the team's next steps include testing integration into watches and smart jewelry.

Nov 5Thu
  1. TechCrunch · AI58

    Agility is the first pure humanoid robot company on the stock market. Are you buying?

    Agility is going public through a SPAC reverse merger that will raise $465 million in new capital, making it the first pure humanoid robot company on the stock market. Its Digit robots are doing real industrial work for nine customers, but the company earned just $174,000 in the first six months of 2026 while losing $362 million, and it has $300 million in planned contracts from 30 customers for the next-generation Digit 5, which is set to be demonstrated at CES 2027.

  2. Ars Technica · AI58

    Agility Robotics' Nasdaq debut invites scrutiny of humanoid robot market

    Agility Robotics, headquartered in Salem, Oregon, became the first dedicated humanoid robot developer to go public in the US, listing on Nasdaq as of November 5. In its securities filing the company acknowledged that humanoid robots remain unproven at scale and have not achieved widespread commercialization, unlike industrial robots with decades of proven warehouse and factory track records. Co-founder and chief robot officer Jonathan Hurst told Ars that renting robots as a service lowers customer risk because the rate is comparable to human labor and provides return on investment from day one.

Nov 4Wed
  1. Hacker News AI31

    EdgeDAE: Acceleration of Diffusion Action Experts for Real-Time Physical AI

    EdgeDAE is a heterogeneous FPGA-GPU system that offloads the perception-heavy vision transformer to the GPU while accelerating Diffusion Action Expert (DAE) inference on an FPGA by storing all parameters on-chip in BRAM/URAM, eliminating the repeated DRAM parameter loading of iterative denoising. It co-designs quantization, fixed-point arithmetic, and hardware-efficient random number generation for the FPGA fabric. Compared with an edge GPU baseline, it reduces end-to-end inference latency by 52.5% for Octo-Small and 38.7% for Octo-Base with up to 2.10x higher throughput, and achieves about 17x higher energy efficiency than an RTX 4090.

  2. Hacker News AI38

    Why Physical AI Is So Hard to Engineer

    Physical AI—autonomous machines that sense, reason and act in the real world—requires a full systems approach spanning sensors, actuators and distributed compute, with real-time predictability and safety becoming far more critical than raw performance. Industry experts from Synopsys, Cadence, Arm, Renesas, MediaTek and others describe challenges including hardware/software co-design, modularity and future-proofing for long-lived systems, and unpredictable environments, with PwC projecting a ~€430 billion market by 2030. The article argues that success depends on new architectures, methodologies and verification approaches rather than simply faster chips or better models.