| --- |
| task_categories: |
| - robotics |
| tags: |
| - code |
| size_categories: |
| - 100B<n<1T |
| --- |
| # Robotic Manipulation Datasets for Four Tasks |
|
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| [[Project Page]](https://data-scaling-laws.github.io/) |
| [[Paper]](https://data-scaling-laws.github.io/paper.pdf) |
| [[Code]](https://github.com/Fanqi-Lin/Data-Scaling-Laws) |
| [[Models]](https://huggingface.co/Fanqi-Lin/Task-Models/) |
|
|
| This repository contains in-the-wild robotic manipulation datasets collected using [UMI](https://umi-gripper.github.io/), and processed through a SLAM pipeline, as described in the paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation". The datasets cover four tasks: |
| + Pour Water |
| + Arrange Mouse |
| + Fold Towel |
| + Unplug Charger |
|
|
| ## Dataset Folders: |
| **arrange_mouse** and **pour_water**: Each folder contains data from 32 unique environment-object pairs, with 120 demonstrations per pair. |
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| **fold_towel** and **unplug_charger**: Each folder contains data from 32 unique environment-object pairs, with 60 demonstrations per pair. |
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| **pour_water_16_env_4_object** and **arrange_mouse_16_env_4_object**: These folders contain data from 16 environments, with 4 different manipulation objects per environment, and 120 demonstrations per object. |
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| These datasets can be used to train policies that generalize effectively to novel environments and objects. |
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| For more details on how to use our datasets, please refer to our [code](https://github.com/Fanqi-Lin/Data-Scaling-Laws). |
