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--- |
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task_categories: |
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- robotics |
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tags: |
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- code |
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size_categories: |
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- 100B<n<1T |
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--- |
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# Robotic Manipulation Datasets for Four Tasks |
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[[Project Page]](https://data-scaling-laws.github.io/) |
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[[Paper]](https://huggingface.co/papers/2410.18647) |
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[[Code]](https://github.com/Fanqi-Lin/Data-Scaling-Laws) |
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[[Models]](https://huggingface.co/Fanqi-Lin/Task-Models/) |
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[[Raw GoPro Videos]](https://huggingface.co/datasets/Fanqi-Lin/GoPro-Raw-Videos) |
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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: |
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+ Pour Water |
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+ Arrange Mouse |
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+ Fold Towel |
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+ Unplug Charger |
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## Dataset Folders: |
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**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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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: |
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```shell |
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cat pour_water_16_env_4_object/dataset_part_* > pour_water_16_env_4_object/dataset.zarr.zip |
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``` |
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## Additional Information |
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+ 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. |
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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). |
