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Only Research#NVIDIA
  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. arXiv cs.RO78

    NVIDIA proposes Cosmos-based interactive world simulation framework

    NVIDIA proposes a shared framework that adapts Cosmos world foundation models for real-time interactive world simulation for Physical AI. The framework can stream visual observations in response to actions and scene control flows, supporting both predicting the visual consequences of actions and generating observations from physical states maintained by an external simulator. The authors demonstrate it in camera navigation, robot manipulation, controllable video transfer, and multi-view autonomous driving simulation, and have released the code and model checkpoints.

    Why it matters: NVIDIA has turned the Cosmos world foundation model into a real-time interactive simulation framework and released the code and weights, allowing readers to judge the usable boundaries of world models in robot training.

  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. 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. 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.

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