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--- |
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library_name: pytorch |
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tags: |
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- robotics |
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- libero |
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- vision-language-action |
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- imitation-learning |
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- manipulation |
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datasets: |
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- gate-institute/GATE-VLAP-datasets |
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--- |
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# GATE-VLAP: Grounded Action Trajectory Embeddings with Vision-Language Action Planning |
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**Trained on LIBERO-10 Benchmark** |
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This model is trained for robotic manipulation tasks using vision-language-action learning with semantic action chunking. |
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## Model Details |
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- **Architecture**: CLIP-RT (CLIP-based Robot Transformer) |
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- **Training Dataset**: [GATE-VLAP LIBERO-10](https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets) |
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- **Training Epochs**: 90 |
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- **Task Type**: Long-horizon robotic manipulation |
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- **Input**: RGB images (128×128) + language instructions |
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- **Output**: 7-DOF actions (xyz, rpy, gripper) |
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## Training Details |
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- **Dataset**: LIBERO-10 (29 subtasks, 1,354 demonstrations) |
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- **Segmentation**: Semantic action chunking using Gemini Vision API |
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- **Framework**: PyTorch |
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- **Checkpoint**: Epoch 90 (best_epoch) |
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## Performance |
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Training run: `libero_10_fixed_training_v1` |
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*Overall performance accuracy: 88.8 % task success rate => 5 % better than raw CLIP-RT on LIBERO-LONG* |
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## Dataset |
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This model was trained on the [GATE-VLAP Datasets](https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets), which includes: |
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- LIBERO-10: 103,650 frames across 29 subtasks |
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- Semantic action segmentation |
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- Vision-language annotations |
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## Citation |
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```bibtex |
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@article{gateVLAP@SAC2026, |
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title={Atomic Action Slicing: Planner-Aligned Options for Generalist VLA Agents}, |
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author={Stefan Tabakov, Asen Popov, Dimitar Dimitrov, Ensiye Kiyamousavi and Boris Kraychev}, |
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journal={arXiv preprint arXiv:XXXX.XXXXX}, |
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conference={The 41st ACM/SIGAPP Symposium On Applied Computing (SAC2026), track on Intelligent Robotics and Multi-Agent Systems (IRMAS)}, |
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year={2025} |
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} |
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``` |
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## Maintainer |
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[**GATE Institute**](https://www.gate-ai.eu/en/home/) - Advanced AI Research Group, Sofia, Bulgaria |
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## Links |
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- 🤗 **Dataset**: [gate-institute/GATE-VLAP-datasets](https://huggingface.co/datasets/gate-institute/GATE-VLAP-datasets) |
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- 📄 **Paper**: *Coming soon* |
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- 💻 **Code**: *Coming soon* |
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