בניית צינור למידה רובוטית בזרימה

תקציר מקורי באנגליתIn this tutorial, we design an end-to-end streaming robotics learning pipeline around the NVIDIA Cosmos3-DROID dataset without downloading its 707 GB repository locally. We first introspect the LeRobotDataset v3.0 structure and construct a metadata graph from info.json, task metadata, episode tables, and dataset statistics, then use HTTP byte-range access with PyArrow to selectively read Parquet row groups and columns. We convert individual episodes into state-action trajectories and analyze joint motion, gripper events, Cartesian end-effector paths, and action-frequency spectra before decoding only the required AV1 video windows through seek-based PyAV/FFmpeg access. We then normalize observations and actions using dataset statistics, construct an ACT-style chunked PyTorch dataset with op