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README.md
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- [Folder hierarchy](#folder-hierarchy)
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- [Details](#details)
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- [Download the Dataset](#download-the-dataset)
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- [
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- [License and Citation](#license-and-citation)
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# [About the Dataset](#contents)
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- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
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# [Dataset Structure](#contents)
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| index | [N, 1] | Global unique index across all frames in the dataset |
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| task_index | [N, 1] | Index identifying the task type being performed |
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# [Download the Dataset](#contents)
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### Python Script
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--local-dir "/where/you/want/to/save"
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```
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# [
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## Load the dataset
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Choose the appropriate import based on your version:
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dataset = LeRobotDataset(repo_id='where/the/dataset/you/stored')
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```
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You need to migrate the dataset from v2.1 to v3.0 first. See the official documentation: [Migrate the dataset from v2.1 to v3.0](https://huggingface.co/docs/lerobot/lerobot-dataset-v3)
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- [Folder hierarchy](#folder-hierarchy)
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- [Details](#details)
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- [Download the Dataset](#download-the-dataset)
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- [Load the Dataset](#get-started)
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- [License and Citation](#license-and-citation)
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# [About the Dataset](#contents)
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- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot)
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- **~200 hours real world scenarios** across **1** main task, **3** sub tasks
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- A clothing organization task that involves identifying the type of clothing and determining the next action based on its category
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- **sub-tasks**
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- **Folding**
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- Randomly pick a piece of clothing from the basket and place it on the workbench
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- If it is a short T-shirt, fold it
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- **Hanging Preparation**
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- Randomly pick a piece of clothing from the basket and place it on the workbench
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- If it is a dress shirt, locate the collar and drag the clothing to the right side
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- **Hanging**
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- Hang the dress shirt properly
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# [Dataset Structure](#contents)
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| index | [N, 1] | Global unique index across all frames in the dataset |
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| task_index | [N, 1] | Index identifying the task type being performed |
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## [task.jsonl](#meta/tasks.jsonl)
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positive/negitive: Labels indicating the advantage of each frame's action, where "positive" means the action benefits future outcomes and "negative" means otherwise.
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# [Download the Dataset](#contents)
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### Python Script
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--local-dir "/where/you/want/to/save"
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```
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# [Load the dataset](#contents)
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## For LeRobot version < 0.4.0
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Choose the appropriate import based on your version:
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dataset = LeRobotDataset(repo_id='where/the/dataset/you/stored')
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```
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## For LeRobot version >= 0.4.0
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You need to migrate the dataset from v2.1 to v3.0 first. See the official documentation: [Migrate the dataset from v2.1 to v3.0](https://huggingface.co/docs/lerobot/lerobot-dataset-v3)
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