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#Guides and practice
  1. Reddit r/robotics78

    PrismaX shares π0.5 policy transfer across YAM devices: fine-tuning ablations, latency bug, and 16GB VRAM deployment

    The PrismaX team shared its practice of transferring a π0.5 policy trained on one YAM device to another YAM device, covering three parts: fine-tuning ablations, latency troubleshooting, and VRAM compression. On fine-tuning, training only the action expert yielded a 51% grasp rate and a 12% drop rate; full fine-tuning gave 89% and 53%; r=128 LoRA gave 84% and 59% while using 32GB. Strong lighting roughly halved the performance of the checkpoint trained only on the action expert.

    Why it matters: The original post provides details on fine-tuning ablations, latency troubleshooting, and VRAM compression for π0.5 policy cross-device transfer, which can be reused on existing equipment in small labs.

  1. Xbotics具身智能实验室 · 微信公众号77

    How to Choose a Robot Simulator: Same Success, Tenfold Contact Force Gap

    The article interprets a simulator comparison report released by Manda Robotics in early October, running about ten sets of experiments with the same Franka 7-axis robotic arm and the same control program on four physics engines: PhysX, MuJoCo, MuJoCo Warp, and Genesis.

    Why it matters: The article uses controlled comparison experiments under uniform conditions to show that simulator divergence is concentrated in contact forces rather than success or failure, and provides transferable troubleshooting and verification methods.

  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. RoboParty · 微信公众号75

    RoboParty Lab Established, Open-Sources Three Humanoid Robot Toolchains for Training, Control, and Data

    RoboParty announced the establishment of RoboParty Lab and open-sourced three humanoid robot toolchains: the supervised learning training framework MimicLite, the unsupervised reinforcement learning control framework UFO, and the motion retargeting tool hhtools.

    Why it matters: The three toolchains provide specific figures on training compute, time required, and the policies already integrated, which readers can use to assess their reuse value in their own R&D workflows.

  1. Toyota Research Institute Robotics77

    Toyota Research Institute explains statistical thinking for robot policy evaluation: from rigorous A/B testing to effective visualization

    The Toyota Research Institute (TRI) published a tutorial article introducing a statistical A/B testing method based on success/failure labels for robot policy evaluation.

    Why it matters: The TRI authors systematically explain statistical pitfalls and reusable tools in robot policy A/B testing, with runnable Python examples.

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