| --- |
| 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) |
|
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| This repository contains four models for the manipulation tasks described in the paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation". |
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| The tasks include: |
| + Arrange Mouse |
| + Fold Towel |
| + Pour Water |
| + Unplug Charger |
|
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| 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. |
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| For details on how to use these models, please refer to our [code](https://github.com/Fanqi-Lin/Data-Scaling-Laws). |
