---
license: mit
task_categories:
- robotics
tags:
- code
size_categories:
- 100B<n<1T
---
# Raw GoPro Videos for Four Robotic Manipulation 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/)
[[Processed Dataset]](https://huggingface.co/datasets/Fanqi-Lin/Processed-Task-Dataset)

This repository contains raw GoPro videos of robotic manipulation tasks collected in-the-wild using [UMI](https://umi-gripper.github.io/), as described in the paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation". The dataset covers four tasks:
+ Pour Water
+ Arrange Mouse
+ Fold Towel
+ Unplug Charger

## Dataset Folders:
**arrange_mouse** and **pour_water**: Each folder contains data collected from 32 environments.
+ The first 16 environments have 4 different object folders per environment, each containing 120 GoPro videos.
+ The remaining 16 environments have one object folder per environment, each containing 120 GoPro videos.

**fold_towel** and **unplug_charger**: Each folder contains data from 32 unique environment-object pairs, with 60 GoPro videos per pair.

## Usage
The raw GoPro videos can be processed using the provided [code](https://github.com/Fanqi-Lin/Data-Scaling-Laws) to create the [processed dataset](https://huggingface.co/datasets/Fanqi-Lin/Processed-Task-Dataset) for direct use in policy learning.