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README.md

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+# 🦾 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/)
 
-Website: liruiw.github.io/hpt
+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).
 
-See the [HPT](https://github.com/liruiw/HPT-Pretrain) GitHub README and the [LeRobot](https://github.com/huggingface/lerobot) Implementation for instructions on how to use this checkpoint for fine-tuning.
 
+**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!
 
-BibTeX:
 
+If you find HPT useful in your research, please consider citing:
+```
 @inproceedings{wang2024hpt,
-author={Lirui Wang, Xinlei Chen, Jialiang Zhao, Kaiming He, Russ Tedrake},
-title={Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers},
-year={2024},
-eprint={2407.16677},
-archivePrefix={arXiv},
-primaryClass={cs.RO},
-url={https://arxiv.org/abs/2407.16677}}
+author    = {Lirui Wang, Xinlei Chen, Jialiang Zhao, Kaiming He},
+title     = {Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers},
+booktitle = {Neurips},
+year      = {2024}
+}
+```
 
 
-Contact
+## Contact
 
-Lirui Wang (liruiw@mit.edu)
+If you have any questions, feel free to contact me through email (liruiw@mit.edu). Enjoy!