Hi @kh-ryu 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), you can also claim the paper as yours which will show up on your public profile at HF, and add Github and project page URLs.
I saw on your project page that the code and policies for CRAFT are "Coming soon". Would you be interested in hosting the pre-trained reinforcement learning policies (checkpoints) for the quadruped navigation and bimanual manipulation tasks on https://huggingface.co/models?
I noticed you mentioned using SKRL for your implementation. SKRL has a direct integration with Hugging Face, making it very easy to push and load checkpoints directly from the Hub with just a few lines of code.
Hosting on Hugging Face will give you more visibility and enable better discoverability. We can add tags like reinforcement-learning and robotics so that people find the models easier, and link them directly to the paper page.
If you're down, leaving a guide here.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ HF 🤗
Hi @kh-ryu 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability. If you are one of the authors, you can submit it at https://huggingface.co/papers/submit.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models for instance), you can also claim the paper as yours which will show up on your public profile at HF, and add Github and project page URLs.
I saw on your project page that the code and policies for CRAFT are "Coming soon". Would you be interested in hosting the pre-trained reinforcement learning policies (checkpoints) for the quadruped navigation and bimanual manipulation tasks on https://huggingface.co/models?
I noticed you mentioned using SKRL for your implementation. SKRL has a direct integration with Hugging Face, making it very easy to push and load checkpoints directly from the Hub with just a few lines of code.
Hosting on Hugging Face will give you more visibility and enable better discoverability. We can add tags like
reinforcement-learningandroboticsso that people find the models easier, and link them directly to the paper page.If you're down, leaving a guide here.
Let me know if you're interested or need any guidance!
Kind regards,
Niels
ML Engineer @ HF 🤗