--- datasets: - Fanqi-Lin/Processed-Task-Dataset metrics: - accuracy pipeline_tag: robotics --- # Robotic Manipulation Models 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) [[Processed Dataset]](https://huggingface.co/datasets/Fanqi-Lin/Processed-Task-Dataset) [[Raw GoPro Videos]](https://huggingface.co/datasets/Fanqi-Lin/GoPro-Raw-Videos) This repository contains four models for the manipulation tasks described in the paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation". The tasks include: + Arrange Mouse + Fold Towel + Pour Water + Unplug Charger For each task, we release a policy trained on data collected from 32 unique environment-object pairs, with 50 demonstrations per environment. These policies have been shown to generalize effectively to novel environments and objects. For details on how to use these models, please refer to our [code](https://github.com/Fanqi-Lin/Data-Scaling-Laws).