Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- clip-rt/modified_libero_hdf5
+language:
+- en
+tags:
+- robotics
+- vla
+- clip
+- contrastive_learning
+---
+
+# CLIP-RT Finetuned on LIBERO-Goal
+
+We finetune the original [CLIP-RT model](https://clip-rt.github.io/) with a 300M-parameter action decoder to enable continuous action prediction. This checkpoint is the model finetuned on [LIBERO](https://libero-project.github.io/main.html) goal task suite.
+
+
+## Hyperparemeters
+
+| Category             | Details                                                             |
+|----------------------|---------------------------------------------------------------------|
+| **Train**            | 8 × H100 GPUs, each with 80GB VRAM (batch size: 256)                |
+| **Model size**       | 1.3B (CLIP-RT base + 0.3B action decoder)                           |
+| **Action dimension** | 7D end-effector action × 8 action chunks                            |
+| **Loss**             | L1 regression                                                       |
+| **Epochs**           | 128                                                                 |
+| **Performance**      | 92.2% success rate on the LIBERO-Goal task suite                    |
+| **Throughput**       | 163Hz                                                               |
+| **Inference**        | One GPU with 9GB VRAM                                               |
+
+## Usage Instructions
+If you want to evaluate this model on the LIBERO simulator, please refer to the [clip-rt github repository](https://github.com/clip-rt/clip-rt/tree/main/libero). 
+
+## Citation
+
+```bibtex
+@article{kang2024cliprt,
+  title={CLIP-RT: Learning Language-Conditioned Robotic Policies from Natural Language Supervision},
+  author={Kang, Gi-Cheon and Kim, Junghyun and Shim, Kyuhwan and Lee, Jun Ki and Zhang, Byoung-Tak},
+  journal={arXiv preprint arXiv:2411.00508},
+  year = {2024}
+}
+```