Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Any4LeRobot dataset conversions, LeRobot-to-RLDS export, and LeRobot v1.6-v3.0 format migrations with schema validation, version-aware environments, and safe publication boundaries.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Any4LeRobot is a script-oriented toolbox for moving robotics datasets into and between LeRobot formats. Use this skill to plan a conversion from a documented source layout, select the matching route, validate schemas before writing, and keep Ray, Beam, simulator, video, and Hub side effects explicit.
scripts/check_environment.py before
executing any converter. It reports core and optional import/API status but
never downloads data or writes a dataset.This skill contains distilled guidance, not a dependency on the original
checkout. Do not tell a future agent to run a source-repository script or
checkout-relative convert.sh; use the route references and their commands as
recipes, then verify the equivalent entry point in the selected environment.
| User request or input | Read this route |
|---|---|
| Design an adapter, use local/Ray DataTrove execution, aggregate task outputs, resume, or publish | generic-conversion |
| Open X-Embodiment/RLDS/TFDS → LeRobot | openx-conversion |
| AgiBotWorld raw tree → LeRobot | agibot-conversion |
| RoboMIND benchmark/embodiment HDF5 → LeRobot | robomind-conversion |
| LIBERO HDF5 → LeRobot or LIBERO rerender planning | libero-conversion |
| RoboCasa HDF5, subset, depth/segmentation, or rerender planning | robocasa-conversion |
| LeRobot → RLDS/TFDS | rlds-export |
| LeRobot v1.6, v2.0, v2.1, or v3.0 migration | version-migration |
Do not conflate the two RLDS directions: openx-conversion consumes an RLDS
builder, while rlds-export produces one. Do not use version-migration to
convert raw HDF5 or to repair missing modalities.
Any4LeRobot has no pyproject.toml, setup.py, or declared distribution. The
usual baseline is a private Python 3.10/3.11 environment with a LeRobot writer
compatible with the selected source script, NumPy, h5py, pyarrow/pandas,
Pillow/OpenCV, tqdm, and the video codec stack. Add only what the route needs:
datatrove for the shared pipeline; Ray/DataTrove Ray extras only for
distributed execution.tensorflow and tensorflow-datasets for either RLDS direction; Apache Beam
is optional and should be disabled for small/lossless exports.datasets, jsonlines, safetensors, and the exact historical LeRobot
revision for version migration. Keep v2.x and v3.x environments separate.Verify the installed API rather than trusting a package import alone:
python -c "from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata; print('LeRobot API OK')"
python -c "import h5py, pyarrow, torch, torchcodec; print('core dependencies OK')"
The source snapshot mixes historical and current LeRobot import locations. In
particular, a current release may not re-export LeRobotDataset from
lerobot.datasets or provide lerobot.datasets.dataset_writer.py. Treat that
as a compatibility stop gate; do not patch imports or metadata behavior by
guesswork. Read the route-specific troubleshooting before selecting another
revision.
--push-to-hub, Hub deletion/tagging, Git-LFS moves, or public
upload during the first local pass. Use a new staging root or test branch.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。