README.md

# 🦾 Heterogenous Pre-trained Transformers
 
[Lirui Wang](https://liruiw.github.io/), [Xinlei Chen](https://xinleic.xyz/), [Jialiang Zhao](https://alanz.info/), [Kaiming He](https://people.csail.mit.edu/kaiming/)

Neural Information Processing Systems (Spotlight), 2024

You can find more details on our [project page](https://liruiw.github.io/hpt). An alternative clean implementation of HPT in Hugging Face can also be found [here](https://github.com/liruiw/lerobot/tree/hpt_squash/lerobot/common/policies/hpt).


**TL;DR:** HPT aligns different embodiment to a shared latent space and investigates the scaling behaviors in policy learning. Put a scalable transformer in the middle of your policy and don’t train from scratch!


If you find HPT useful in your research, please consider citing:
```
@inproceedings{wang2024hpt,
author    = {Lirui Wang, Xinlei Chen, Jialiang Zhao, Kaiming He},
title     = {Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers},
booktitle = {Neurips},
year      = {2024}
}
```


## Contact

If you have any questions, feel free to contact me through email (liruiw@mit.edu). Enjoy!