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Only First party#NVIDIA
  1. NVIDIA75

    NVIDIA Cosmos Expands with New Tools for Training, Evaluating and Deploying World Models

    NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints for training, evaluating and deploying world models.

    Why it matters: The original lists Cosmos's new training tools, simulation checkpoints and data blueprints, and gives specific data on users such as Porsche and ANYbotics, which can be used to assess changes in the world model development workflow.

  1. Agile Robots/思灵机器人68

    Agile Robots and NVIDIA unveil compact humanoid reference platform Agile ONE SN at GTC

    At NVIDIA GTC, Agile Robots and NVIDIA unveiled Agile ONE SN, a compact humanoid reference platform for R&D, built on the NVIDIA Isaac GR00T development platform and integrating a complete workflow spanning demonstration capture, simulation, training, evaluation, deployment, and real-world testing.

    Why it matters: The original article lists the specific configuration of the robot's whole-body degrees of freedom, compute power, and preconfigured software stack, which can be used to assess how this integrated solution for R&D labs connects with the industrial model.

  2. NVIDIA62

    NVIDIA releases OpenShell reference architecture for building governed AI agents

    At GTC Berlin, NVIDIA announced that the OpenShell reference architecture is now available for building, isolating, observing, and governing secure AI agents on enterprise infrastructure and edge systems.

    Why it matters: The original text explains the reference architecture's layered design and millisecond-level isolation mechanism, which helps readers understand how it brings agent security down to the hardware layer.

  3. NVIDIA80

    NVIDIA Expands Isaac Platform with New Humanoid Reference Platform, Isaac OS, and Isaac Lab 3.0

    At GTC Berlin, NVIDIA announced an expansion of the Isaac open-source robotics development platform, adding the Isaac OS runtime, the officially released Isaac Lab 3.0 learning framework, and an open-source reference workflow based on DGX Station.

    Why it matters: The original text lists the runtime, learning framework, and open-source workflow newly added to the Isaac platform, allowing readers to judge the specific changes in the data-to-deployment pipeline.

  1. Agility Robotics66

    Agility Robotics Releases Next-Generation Humanoid Robot Digit 5

    Agility Robotics has released Digit 5, a next-generation general-purpose humanoid robot built around a safety architecture that allows it to work in close proximity to people without traditional physical safety fencing. It uses an independent safety controller, AI-based human detection, and audiovisual cues, and is the first launch partner for the NVIDIA Halos for Robotics platform, built on NVIDIA IGX Thor.

    Why it matters: The official announcement details Digit 5's safety architecture, payload, battery life, and other specific upgrades, along with order background, giving readers insight into the product approach for humanoid robots working near people.

  1. Skild AI79

    Skild AI annual recurring revenue tops $100 million, outlines deployment-first robotics strategy

    Skild AI announced that its annual recurring revenue (ARR) has surpassed $100 million, of which $50 million was confirmed within 10 months of starting deployments. It has more than 60 paying customers, covering material handling, delivery, inspection, security, food service, and warehouse, factory, and data center scenarios; about 90% of revenue comes from manipulation tasks, with mobility accounting for 10% (of which AMR solutions account for 4%).

    Why it matters: Beyond the company's self-reported revenue milestone, it also offers concrete lessons on how deployment in turn shapes model research, which is useful for understanding the relationship between real-world deployment and research.

  1. Skild AI76

    Skild AI releases robot foundation model S1, achieving in-context learning through video demonstrations

    Skild AI released the robot foundation model S1, which uses video demonstrations as task prompts and can execute manipulation tasks not seen during training without fine-tuning, with long-horizon tasks lasting up to about 10 minutes. At a pretraining data scale of 100,000 hours, the ICL model achieved a 66% success rate on unseen tasks, compared with 9% for the language-prompted VLA; a single demonstration was equivalent to about 380 post-training demonstrations, and the planting task took 11 minutes from recording the demonstration to autonomous execution. S1 was trained on NVIDIA AI infrastructure and has already been put into use with commercial partners.

    Why it matters: The original article provides comparison figures for ICL and VLA at the same data scale and a deployment timeline, allowing readers to assess the feasibility of the in-context learning approach for manipulation tasks.

  1. Agile Robots/思灵机器人61

    Agile Robots and Idealworks detail edge AI inference stack, running VLMs on Jetson AGX Xavier with custom TensorRT plugins

    Agile Robots and its subsidiary Idealworks describe their edge AI deployment stack since 2018, covering five layers: hardware, BSP, inference pipeline, inference framework, and quantization.

    Why it matters: Using their own Xavier deployment experience as an example, the authors present a reusable TensorRT plugin method for running VLMs on older hardware that peers can directly draw on.

  1. HUMANOID62

    Siemens and Humanoid test HMND 01 humanoid robot for autonomous bin handling at German factory

    Siemens and Humanoid announced that Humanoid's HMND 01 wheeled Alpha humanoid robot has completed testing in logistics operations at Siemens' electronics factory in Erlangen, Germany, autonomously performing tasks to grasp, move, and place bins for operators.

    Why it matters: Specific capacity and success-rate metrics were disclosed, allowing readers to gauge the maturity of humanoid robots in real factory logistics operations.

  1. Agile Robots/思灵机器人66

    Agile Robots and Google DeepMind announce strategic partnership to integrate Gemini Robotics models into industrial robots

    Agile Robots SE and Google DeepMind announced a strategic research partnership to combine the Gemini Robotics foundation models with Agile Robots' industrial robot platform and develop adaptable, reasoning-capable robots. The partnership initially focuses on industrial manufacturing scenarios where reliability and scale are critical, forming a data flywheel through deployment, data collection, model training, and iteration; Agile Robots says it has installed more than 20,000 robot solutions worldwide.

    Why it matters: The partnership focuses on industrial deployment and data feedback for training, giving readers insight into the path to real-world adoption when foundation models are connected with robot manufacturers.

  2. Berkeley AUTOLAB68

    CaP-X: Benchmarking and Improvement Framework for Robot Manipulation Coding Agents Released

    Teams including Berkeley AUTOLAB propose CaP-X, an open framework for systematically studying Code-as-Policy agents in robot manipulation, centered on the interactive environment CaP-Gym and CaP-Bench, which evaluates 12 frontier models.

    Why it matters: The original text presents a systematic evaluation framework for Code-as-Policy agents and the finding that test-time scaling can mitigate abstraction dependence, offering reference value for research on coding-agent manipulation control.

  1. Skild AI75

    Skild AI partners with ABB Robotics, Universal Robots, and NVIDIA to deploy Skild Brain

    Skild AI announced partnerships with ABB Robotics and Teradyne-owned Universal Robots and MiR to integrate its general-purpose model Skild Brain into industrial robots, eliminating task-by-task programming and using post-training fine-tuning with a small amount of task data.

    Why it matters: Skild AI describes its own partnership deployment approach with ABB, UR, and NVIDIA and assembly task details, letting readers understand how its general-purpose model enters production lines.

  1. Dexterity66

    Dexterity speeds up Foresight visual perception 17x with NVIDIA, from 1508ms to 90ms

    Dexterity says that after NVIDIA acceleration, the visual perception pipeline of its world model Foresight cut per-cycle latency from 1508ms to 90ms, a 17x speedup.

    Why it matters: The original breaks down latency data and optimization methods for each stage of the pipeline, giving robotics developers a GPU acceleration approach they can adapt to their own perception systems.

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