Fanqi-Lin/GoPro-Raw-Videos
Raw GoPro Videos for Four Robotic Manipulation Tasks [Project Page] [Paper] [Code] [Models] [Processed Dataset] This repository contains raw GoPro videos of robotic manipulation tasks collected in-the-wild using UMI, 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… See the full description on the dataset page: https://huggingface.co/datasets/Fanqi-Lin/GoPro-Raw-Videos.
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, 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 to create the processed dataset for direct use in policy learning.