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README.md

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----
-license: mit
-datasets:
-- clip-rt/modified_libero_hdf5
-language:
-- en
-tags:
-- robotics
-- vla
-- clip
-- contrastive_learning
----
-
-# CLIP-RT Finetuned on LIBERO-Spatial
-
-This model was produced by fine-tuning the [CLIP-RT model](https://clip-rt.github.io/) with a 0.3B parameter action decoder added to enable continuous action prediction on the LIBERO-Spatial dataset from the [LIBERO simulation benchmark](https://libero-project.github.io/main.html). 
-
-## Hyperparemeters
-
-| Category             | Details                                                             |
-|----------------------|---------------------------------------------------------------------|
-| **Hardware**         | 8 × H100 GPUs with 80GB memory                                      |
-| **Model size**       | 1.3B (CLIP-RT base + 0.3B action decoder)                           |
-| **Action dimension** | 7D per step × 8 steps (chunked)                                     |
-| **Loss**             | L1 regression                                                       |
-| **Batch size**       | 256                                                                 |
-| **Epochs**           | 128                                                                 |             
-
-## Usage Instructions
-To evaluate this model on the LIBERO simulator or in your own imitation learning pipeline, use the action decoder module with precomputed CLIP image and language embeddings. Refer to the original [CLIP-RT GitHub repository](https://github.com/clip-rt/clip-rt) for code and inference scripts.
-
-## 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}
-}
-```
+---
+license: mit
+---

cliprt_libero_spatial.pt

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