Create README.md

README.md

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+---
+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.