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Nov 14Sat
  1. 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.

  2. 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.

Nov 10Tue
  1. 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.

  2. 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.

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. 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.

  3. 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 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. 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.

  3. 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.

  4. 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.

Nov 2Mon
  1. Sakana AI Blog47

    [Event Report] Sakana AI Symposium: Jürgen Schmidhuber on the Past and Future of AI, and Language Models, World Models, and Physical AI from Japan

    On October 26, 2026, Sakana AI held a symposium at Hitotsubashi Hall in Tokyo featuring a keynote by Chief Scientific Advisor Jürgen Schmidhuber and a conversation with CEO David Ha, drawing over 400 attendees from across Japan. Sakana AI announced it is moving into pre-training its own models, unveiling the "Sakana Sawara" (provisional name) language model series, with the small first model "Sawara Small" (provisional name) planned for release in spring 2027 if development proceeds smoothly. The report presents edited transcripts of the talks, including Takuya Akiba on LLM development at the RSI Lab, Robert Tjarko Lange on open-ended autonomous discovery systems, and Stefania Druga and Sebastian Risi on world models and Physical AI.

Oct 14Wed
  1. Google DeepMind85

    Gemini 4 Argon for Robotics

    Google introduces Gemini 4 Argon, a general-purpose physical reasoning model that scores 87% on the Embodied Reasoning (ERQA) benchmark and is demonstrated across robot control, Real2Sim reconstruction, physical understanding, and research self-improvement workflows. The post shares representative prompts spanning Spot mobile manipulation with MuJoCo practice, zero-shot arm control, video world model steering, sim twin reconstruction from robot, human, and sketch inputs, hindsight labeling, value functions, and simulation-generated training data that raised real-world success rate from 56% to 75%. Argon is available via Google AI Studio, the Gemini API (gemini-4-argon), and Google Antigravity.

    Why it matters: It offers a detailed, prompt-level look at how a frontier model is being applied across the robotics stack, useful for teams evaluating embodied AI tooling.