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Only Research#NVIDIA
Nov 9Mon
  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.

Nov 2Mon
  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.

Oct 29Thu
  1. CVer · 微信公众号83

    Harvard, Nvidia and others propose RAX: robot agent online exploration, leading by 37.4 points on average across five benchmarks

    A research team from Harvard, Nvidia, Berkeley, MIT and Stanford proposed 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 skills, while keeping model parameters frozen.

    Why it matters: RAX uses a frozen model plus online exploration to generate reusable programs, offering a robot learning path that does not depend on action demonstrations.

Oct 28Wed
  1. The Robot Report78

    WorldOracle releases action-conditioned world model benchmark; seven models show deviations between predictions and real execution

    Princeton University, UT Austin, the University of Toronto, NVIDIA, and AgiBot jointly launched WorldOracle, an action-conditioned world model benchmark that uses the same initial frame and action sequence to compare model predictions with real execution.

    Why it matters: The benchmark compares world model predictions frame by frame against real execution of the same actions and releases failure samples, making it easier to identify sources of model deviation over long sequences.

Oct 27Tue
  1. arXiv cs.RO72

    Zero-HLM: A humanoid loco-manipulation framework without real robot data

    Zero-HLM proposes a real-to-sim-to-real framework that turns smartphone captures and simulated source demonstrations into scalable training data for humanoid loco-manipulation. The study generated 4,800 physically validated demonstrations and achieved zero-shot transfer on three Unitree G1 tasks; GR00T N1.7 trained in simulation reached a mean full-task success rate of 66.7%, versus 46.7% with real-data training.

Oct 26Mon
  1. 机器人产业应用 · 微信公众号62

    NVIDIA Long-WAM and Meta RoboJEPA Submitted on the Same Day: Which Is Worth More, Long Memory or Scaling Laws?

    Long-WAM from teams at NVIDIA, MIT, the University of Hong Kong, and UCSD, and RoboJEPA from Meta FAIR, Mila, and other institutions were submitted to arXiv on the same day, October 7, measuring the returns from adding visual history and from expanding training compute, respectively.

  2. 深蓝AI · 微信公众号75

    NVIDIA and Simon Fraser University propose GATOR: reconstructing posed 3D assets from casual photos

    A NVIDIA team, together with Simon Fraser University, proposes GATOR, which uses generative reconstruction as a base and multimodal agents for local refinement to reconstruct complete 3D objects with scene poses from casually taken photos. The method is trained only on synthetic scenes and, without any real-world fine-tuning, leads baselines on geometry, appearance, and pose metrics in real tabletop and indoor scans; without refinement, it takes about 0.5 minutes to outperform GPT-6-Astra running for 20 minutes. The authors also note that reliance on synthetic data, binding to commercial APIs, and stability under extreme poses remain open issues for deployment.

Oct 25Sun
  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.

Oct 23Fri
  1. 机器之心 · 微信公众号58

    Shanghai Jiao Tong University and other institutions propose Physical RSI framework, using L0–L5 levels to measure robot self-improvement

    Researchers from Shanghai Jiao Tong University, UC Berkeley, Stanford University and other institutions proposed the Physical RSI framework in the paper "Who Improves the Robot? Toward Physical Recursive Self-Improvement," incorporating task capability updates and improvement process updates into a unified analysis and presenting an L0–L5 grading scale.

Oct 21Wed
  1. HuggingFace Daily Papers63

    JCA model jointly generates 3D motion and video for arbitrary skeletons

    An NVIDIA research team proposes the JCA model, which can simultaneously generate video and replayable 3D motion for any given skeleton, and output a camera that registers the motion to the scene. The model keeps 3D motion in a separate model that supports arbitrary skeletons, connects to a pretrained video model through a small number of cross-attention layers, and the motion model first learns skeleton movement from motion-only data.

  3. arXiv cs.RO67

    Idealworks and NVIDIA evaluate sim-to-real transfer of COMPASS navigation

    Idealworks and NVIDIA systematically evaluated the sim-to-real transfer of the X-Mobility and COMPASS learned navigation pipeline on industrial mobile robots. After training in a digital twin with Cosmos-generated sim-to-real data, real-robot success rates across four scenarios rose from 65.3% to 91.7%, weighted travel time nearly halved, the gap to the production-line navigation stack was 8.3 percentage points, and everything ran on the onboard Jetson AGX Xavier.

Oct 20Tue
  1. 雷科技73

    X Square Robot releases X2Real simulation benchmark, with 0.96 correlation between simulated and real-robot task success rates

    X Square Robot released the X2Real simulation benchmark for evaluating the performance of embodied models in the real physical world, proposing three design principles: credibility, validity, and objectivity. In testing, X2Real simulation evaluation achieved a 0.96 correlation with real-robot evaluation in task success rate and a 0.99 correlation in progress score; control parameter calibration achieved millimeter-level trajectory consistency, covering embodiments including Franka, ArtiXon Arm-6A, ARX R5, and Quanta X1.

Oct 19Mon
  1. 36Kr Embodied Intelligence66

    AgiBot, NVIDIA, and universities launch WorldOracle world-model evaluation platform

    WorldOracle, a world-model evaluation platform jointly developed by researchers from AgiBot, NVIDIA, and several universities, is now live and open for evaluation submissions. The platform uses 335 real manipulation trajectories, with about 40% of interaction segments containing failed or suboptimal operations, testing model prediction against real execution outcomes. Testing of 7 world models found that while some models can reproduce robotic-arm motions relatively accurately, their predictions of object states still show considerable deviation, and performance drops markedly on long-horizon prediction.

  2. 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.

  3. arXiv cs.RO70

    WorldOracle: Benchmarking action-conditioned world models against real-world executions

    WorldOracle is a benchmark for action-conditioned world models (ACWMs) that uses identical initial observations and low-level action sequences to test whether predicted rollouts can reproduce real-world physical consequences and task-state transitions. The evaluation covers four dimensions: physical state correctness, interaction spatial alignment, key interaction area fidelity, and multi-view action coherence, and is paired with long, multi-segment real-world data containing both successful and failed executions. Experiments on seven representative ACWMs reveal different models' strengths and failure modes. The dataset, evaluation code, and leaderboard are publicly available.

Oct 16Fri
Jun 18Thu
  1. Ars Technica65

    Nvidia GEAR develops ENPIRE framework, enabling AI coding agents to autonomously train robots to cut zip ties and install GPUs

    Researchers at Nvidia GEAR lab, Carnegie Mellon University, and UC Berkeley developed an agent framework called ENPIRE that lets AI coding agents autonomously schedule and iterate robot training; the robots learned to cut zip ties and insert GPUs into narrow, thin slots on a motherboard.

Mar 24Tue
  1. 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.

Mar 18Tue
  1. FieldAI39

    FieldAI Research Institute (FAIRI) studies embodied question answering in dynamic environments based on the NVIDIA Omniverse platform

    Field AI Research Institute (FAIRI) is collaborating with NVIDIA to study robotic embodied question answering (EQ&A) in dynamic environments. The team uses the NVIDIA Omniverse platform, Isaac Sim, Omniverse Replicator, and the USD Search API to build physically accurate 3D office scenes, simulate changes in the environment over time, and plans to open-source this long-term embodied question answering dataset.