| --- |
| task_categories: |
| - robotics |
| tags: |
| - code |
| size_categories: |
| - 100B<n<1T |
| --- |
| # Robotic Manipulation Datasets for Four Tasks |
|
|
| [[Project Page]](https://data-scaling-laws.github.io/) |
| [[Paper]](https://huggingface.co/papers/2410.18647) |
| [[Code]](https://github.com/Fanqi-Lin/Data-Scaling-Laws) |
| [[Models]](https://huggingface.co/Fanqi-Lin/Task-Models/) |
| [[Raw GoPro Videos]](https://huggingface.co/datasets/Fanqi-Lin/GoPro-Raw-Videos) |
|
|
| 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. |
|
|
| **fold_towel** and **unplug_charger**: Each folder contains data from 32 unique environment-object pairs, with 60 demonstrations per pair. |
|
|
| **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. |
|
|
| Note that due to the size of the pour_water_16_env_4_object/dataset.zarr.zip file (over 50GB), it has been split into two parts. You can restore the full dataset using the following command: |
| |
| ```shell |
| cat pour_water_16_env_4_object/dataset_part_* > pour_water_16_env_4_object/dataset.zarr.zip |
| ``` |
| |
| ## Additional Information |
| + Each dataset is a merge of smaller datasets (one per environment-object pair). Inside each folder, you will find a **count.txt** file that lists the number of demonstrations in each smaller dataset. |
| + These datasets can be used to train policies that generalize effectively to novel environments and objects. |
| + For more details on how to use our datasets, please refer to our [code](https://github.com/Fanqi-Lin/Data-Scaling-Laws). |
