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How to load and explore a robot dataset from the Hub with Lerobot

Load a LeRobotDataset in Python and inspect synchronized video and action data episode by episode.

Short answer: Install the dataset extra with pip install 'lerobot[dataset]'; then load a dataset in Python with LeRobotDataset("lerobot/aloha_mobile_cabinet"), as in the README; Index an episode and print its action shape; video decoding is automatic. All steps.

Who it is forA student or researcher who wants real robot demonstration data to study or reuse.

The problemRobot datasets come in incompatible formats and are hard to stream and inspect.

The outcomeA dataset loaded from the Hub with video decoding handled for you, ready to edit, split or merge.

What you need first

  • Python and pip
  • Extras for your task: lerobot[dataset], lerobot[training], or lerobot[core_scripts,feetech] for an SO-101 arm
  • A GPU for training most policies (see the Compute Hardware Guide)
  • Optional: a supported robot such as SO101, Koch or LeKiwi, and a Hugging Face account to push datasets and models

Steps

  1. Install the dataset extra with pip install 'lerobot[dataset]'
  2. Load a dataset in Python with LeRobotDataset("lerobot/aloha_mobile_cabinet"), as in the README
  3. Index an episode and print its action shape; video decoding is automatic
  4. Use the dataset tools to delete episodes, split by indices or fractions, add or remove features, or merge datasets
  5. Browse thousands more datasets on the Hugging Face Hub under Lerobot

Prompt for your agent

Using huggingface/lerobot, load the dataset lerobot/aloha_mobile_cabinet with LeRobotDataset, print the features and the action shape of episode 0, and split it into train and validation by fraction.

Caveat. This card is written from the huggingface/lerobot README, not from a test run by Deduped. Before you start, make sure you have Python and pip. Check the repo for newer instructions before you rely on these steps.

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