ロボット学習の効率的な仕組み作り

このチュートリアルでは、NVIDIA Cosmos3-DROIDデータセットの707 GBにも及ぶリポジトリをローカルにダウンロードすることなく、エンドツーエンドのストリーミングロボット学習パイプラインを設計します。 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 optional visual conditioning, and train a multimodal behavior-cloning policy。 Finally, we evaluate the learned policy through open-loop rollout with temporally ensembled action chunks, report per-joint MSE and R^2 against a mean-action baseline, visualize predicted versus ground-truth actions, and save the complete policy checkpoint for downstream use。