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#Hugging Face
  1. 量子位80

    Ant Group's Lingbo open-sources LingBot-Touch tactile foundation model

    On November 10, Ant Group's Lingbo Technology open-sourced LingBot-Touch, a tactile foundation model pre-trained on nearly 5,000 hours of real tactile data. It covers three sensing principles—magnetic tactile, visuotactile, and piezoresistive—and outputs unified tactile tokens for use by different dexterous hands and action models.

    Why it matters: The original text provides a unified representation across three types of tactile sensing and data from 26 real-machine tests, which can be used to assess the gains a tactile foundation model brings to existing action models.

  1. HuggingFace Daily Papers78

    PyRUA-DataFarm: Using LLM agents instead of teleoperation to generate real-ready robot data

    PyRUA-DataFarm proposes having a coding agent rebuild tasks in simulation, with a video model rendering them into real-ready data, replacing teleoperation for collecting robot demonstrations. On five real-robot tasks, policies trained only on this data achieved 93/100 successes, versus 84/100 for policies trained on teleoperated data; under an unseen tablecloth and colored lighting, the former kept 87 successes while the latter fell to 46.

    Why it matters: The paper presents a complete pipeline and cost comparison for using a coding agent to replace teleoperation in collecting demonstrations, which can be used to assess the room for replacing this data-collection route.

  1. HuggingFace Daily Papers79

    CaPtain: A Post-Training Pipeline for Code-as-Policy Robot Agents

    CaPtain proposes an open post-training pipeline that trains small open vision-language models such as Qwen3.5-4B into code-as-policy (CaP) robot agents that do not rely on VLAs, frontier models, or privileged state at test time.

    Why it matters: The work proposes using a physical critic to assign credit across multi-turn code policies and presents a complete training procedure for small models on LIBERO and real robots.

  1. 简智机器人 · X67

    GenRobot.AI's two open-source datasets enter Hugging Face robotics dataset trending top five

    GenRobot.AI announced that both of its open-source datasets, Gen-HumanWholeBody and Gen-HumanEgo, have entered the top five of the Hugging Face robotics dataset trending list. Gen-HumanWholeBody is a first-person whole-body manipulation dataset containing 600 hours of whole-body manipulation data, 16,976 real-world scenes, and 1,281 human skills, with whole-body MPJPE of about 3 cm; the authors say it targets whole-body motion learning, human-to-robot transfer, and embodied foundation model research.

    Why it matters: Two open-source datasets entering the top five of the Hugging Face robotics dataset trending list can serve as a reference for observing the level of attention to whole-body manipulation data.

  1. arXiv cs.RO78

    Study: Full-weight VLA fine-tuning without an fp32 master copy silently erases most updates

    The paper points out that when VLA models are fully fine-tuned in bf16 without keeping an fp32 master copy, AdamW rounds away any update smaller than 1/512 of a weight; 9 of 12 open-source VLA codebases train this way by default or in documented recipes, including LeRobot's SmolVLA default configuration and the π0/π0.5 guides.

    Why it matters: The original quantifies the proportion of updates lost in bf16 fine-tuning and provides a LIBERO success-rate comparison for the fp32 master copy, which can serve as a reference for training configurations.

  1. Pollen Robotics · YouTube64

    Pollen Robotics launches Microduck Arena virtual competition arena

    Pollen Robotics has released Microduck Arena, a virtual arena where Microduck robots race, train, and compete without the need for physical robots. Users train policies with reinforcement learning and publish them to their own Hugging Face accounts to enter challenges; each submission is evaluated uniformly by the Arena, and results can be compared across players. Published policies can be downloaded as .onnx files to watch replays and learn from the best runs.

    Why it matters: It connects policy training, unified evaluation, and replay-based learning into a closed loop, letting readers learn how to train and compare reinforcement learning policies without physical robots.

  1. Hugging Face LeRobot82

    LeRobot v0.6.0 released with multiple breaking changes and new policies

    Hugging Face LeRobot has released v0.6.0: pip install lerobot no longer includes dataset and training dependencies by default, requiring extras to be added as needed; GR00T N1.5 has been replaced by N1.7; and the minimum PyTorch version has been raised to 2.7.

    Why it matters: The release notes list specific breaking changes and migration methods, so developers in the middle of upgrading can use them to check their installation, configuration, and training workflows.

  1. Ars Technica79

    Hugging Face launches LeRobot Humanoid 3D-printed bipedal robot platform at about $2,500

    Hugging Face has released the LeRobot Humanoid full-stack project, with a pair of humanoid robot legs priced at $2,500, built from 3D-printed parts and off-the-shelf components. The release includes a bill of materials, 3D printing files, wiring documentation, assembly instructions, and software tools for calibrating and controlling the robot both on the physical body and in simulation.

    Why it matters: The original text explains the hardware cost, the open materials, and the simulation-to-real design loop, which can be used to assess the impact of a low-cost humanoid platform on research workflows.

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