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#NVIDIA
  1. The Robot Report88

    NVIDIA launches GR00T 2 world action model and Isaac Lab 3.0 at CoRL

    NVIDIA launched the open world action model GR00T 2 and the open-source robot learning framework Isaac Lab 3.0 at CoRL. GR00T 2 was trained on 100,000 hours of NVIDIA open-source first-person data; with only one demonstration per task, it reached an average success rate of 71% across 50 tasks, rising to 95% with five demonstrations, and without task-specific demonstrations it completed at least once each of 40 of the 50 tasks.

    Why it matters: GR00T 2's few-shot success rates and Isaac Lab 3.0's training throughput gains provide the current baseline for embodied models and the simulation training stack.

  2. 机器人前瞻 · 微信公众号88

    NVIDIA releases GR00T 2 series world-action models, trained on 400,000 hours of human video

    At CoRL 2026, NVIDIA launched the open-source world-action model series GR00T 2, comprising two versions, GR00T 2 and GR00T 2 Turbo. The former focuses on generalization and fine manipulation, while the latter has fewer parameters and can be deployed directly on the Unitree G1, with a single inference taking 124 milliseconds on the onboard Jetson Thor.

    Why it matters: NVIDIA released the GR00T 2 series at CoRL 2026 and disclosed the scale of three types of pre-training data along with success rates for cross-embodiment transfer and few-shot adaptation, which can be used to assess the current capability boundaries of world-action models.

  1. Tech Xplore Robotics78

    UC Berkeley and NVIDIA propose agentic robotics to generate and improve robot programs in simulation

    Ken Goldberg's team at UC Berkeley and Spencer Huang, NVIDIA's head of robotics engineering, proposed an "agentic robotics" method that lets AI agents generate, test, and correct robot control programs in simulation without additional data collection.

    Why it matters: The report describes a method for using multi-agent systems to generate, test, and correct robot programs in simulation, and presents real-world data from warehouse sorting.

  1. 机器人全球资讯 · 微信公众号80

    Agility Robotics lists on Nasdaq, raising about $465 million

    American humanoid robotics company Agility Robotics officially listed on Nasdaq on November 5, 2026, under the ticker AGLT, going public through a merger with Churchill Capital Corp XI. The transaction and related financing are expected to bring in about $465 million in raised funds, including a PIPE financing of about $201 million led by Foxconn.

    Why it matters: Agility's listing makes its orders, deployment duration, and financing structure public, allowing comparison against the real progress of humanoid robot commercialization.

  2. 机器人前瞻 · 微信公众号80

    NVIDIA releases VFlash visual planning decoding framework, robot action decoding up to 12x faster

    NVIDIA has released VFlash, a general-purpose decoding framework for fast visual planning that replaces token-by-token coordinate generation with parallel decoding of complete visual primitives, allowing it to predict robot grasp positions and motion trajectories in one pass. At batch size 1 on H100, single-generation speed improved by about 25x; across four MolmoSpaces tasks, the world action model averaged a 70.2% success rate versus a 62.6% baseline. Jim Fan shared that it achieves up to 12x faster action decoding, 5.1x overall robot inference speedup, and up to 61% lower 3D trajectory error.

    Why it matters: NVIDIA's new decoding framework changes visual planning from token-by-token generation to parallel prediction, and provides speedup and success-rate comparisons across multiple robot and autonomous-driving benchmarks.

  1. HuggingFace Daily Papers76

    RoboColosseum: A distributed real-world robot policy evaluation framework across four embodiments

    RoboColosseum is a distributed evaluation framework that evaluates general-purpose robot policies over a network of locally operated robots in multiple locations, using unified policy serving and standardized protocols.

    Why it matters: This distributed evaluation framework used 5,699 paired trials to compare VLA policies with frontier agents, revealing inconsistent rankings across embodiments and a ranking reversal after fine-tuning.

  1. 钛媒体81

    Astra's Shockwave and the Embodied AI Industry's 50 Days

    In the 50 days since GPT-6 Astra was released, the embodied AI industry has conducted intensive evaluations and discussions around it, and general-purpose large models have begun directly taking over robot operation. Tests by SenseTime and several institutions concluded that Astra is basically capable of completing embodied tasks, but still has a clear gap from practical use in real environments, underperforming in millimeter-level contact, dynamic control, and response speed. The article also reviews industry uses such as using Astra directly for policies, reviewing VLA actions, building simulation environments, and data annotation, as well as chain reactions including cooling financing and major companies stepping up data collection.

    Why it matters: It reviews evaluation results, company reactions, and industrial chain changes in the embodied AI industry within 50 days of GPT-6 Astra's release, presenting the differentiated landscape after general-purpose large models entered robot operation.

  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. 机器人前瞻 · 微信公众号82

    NVIDIA open-sources robot data engine Flora, labeling cost about 1/125 that of GPT-6 Astra

    NVIDIA released and open-sourced the data engine Flora, which converts raw robot video into training data such as timestamped action descriptions, process rewards, and outcome rewards; it is already used to train the GR00T series models. Flora consists of an action description model, a temporal segmentation model, and a reward model, fine-tuned from the Qwen3.5 series of vision-language models, and can label 200,000 hours of data per day on 512 GB300 GPUs.

    Why it matters: NVIDIA's open-source data engine cuts robot video labeling costs to about 1/125 of a general-purpose large model and provides several benchmark comparisons, which can be used to assess the substitution space in the data labeling segment.

  3. 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.

  4. 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.

  5. Xbotics具身智能实验室 · 微信公众号77

    Isaac Lab 3.0 released: multi-backend architecture, most workflows no longer require Isaac Sim installation

    Isaac Lab 3.0 has been officially released, and the official recommendation is for new projects and projects migrating from 2.x to use v3.0.0 as their base. The new version adopts a multi-backend architecture with a unified task API, supporting backends such as Newton and OV Libraries. Supported tasks can switch physics, rendering, and visualization backends without rewriting logic, and most workflows do not require installing or launching Isaac Sim.

    Why it matters: The original article lists the specific changes in the multi-backend architecture and the removal of the Isaac Sim installation requirement, as well as the environment and interface changes needed for migration, making it easier to judge the upgrade cost for existing projects.

  1. 机器之心 · 微信公众号88

    Harvard, Berkeley, NVIDIA and others propose RAX, using online exploration scaling to let robots learn skills with zero demonstrations

    A research team from Harvard, NVIDIA, Berkeley, MIT and Stanford proposes RAX (Robot Agent eXploration), which connects a general coding agent to robot perception and control interfaces, uses online exploration to write successful decisions into executable programs and accumulate them as reusable skills, while keeping model parameters frozen.

    Why it matters: RAX uses a frozen model plus online exploration to generate reusable programs, leading by an average of 37.4 percentage points on five simulation benchmarks with zero action demonstrations, offering a path for robot learning different from scaling data and models.

  1. 雷科技78

    WorldOracle world-model evaluation platform launched, using real-robot testing to fact-check the industry

    On October 16, WorldOracle, an action-driven world-model evaluation platform jointly completed by researchers from AgiBot, NVIDIA, Princeton University, the University of Texas at Austin, and the University of Toronto, was officially released, opening evaluation submissions to developers.

    Why it matters: The evaluation compares world-model predictions frame by frame against real execution records, revealing a common problem in which robotic arm motion is replicated but object states are systematically hallucinated, providing concrete reference points for judging model usability.

  1. Noble Machines · X75

    NVIDIA Isaac ROS 5.0 released at ROSCon, open source and supporting Jetson Orin Nano through Thor

    NVIDIA released the open-source Isaac ROS 5.0 at ROSCon in Toronto, adding agentic workflows, Isaac Skills, ROS 2 Lyrical support, and GPU-accelerated libraries, with Jetson support spanning Orin Nano through Thor.

    Why it matters: This version update lists GPU-accelerated libraries and inference tools that can be adopted directly on Jetson edge platforms, giving readers a basis for weighing in-house development against adoption.

  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.