Create README.md

README.md

@@ -0,0 +1,31 @@
+---
+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).