---
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://data-scaling-laws.github.io/paper.pdf)
[[Code]](https://github.com/Fanqi-Lin/Data-Scaling-Laws)
[[Models]](https://huggingface.co/Fanqi-Lin/Task-Models/)

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.

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).