Duplicate from Fanqi-Lin/Processed-Task-Dataset

Co-authored-by: Fanqi-Lin <Fanqi-Lin@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+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).

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