Fanqi-Lin/Processed-Task-Dataset

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, 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 pourwater16env4_object/dataset.zarr.zip file (over 50GB), it has been split into two parts. You can restore the full dataset using the following command:

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.