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  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.

  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.

  1. 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.

  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.

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