Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data
Recent advances in imitation learning have enabled robots to learn multiple tasks from large-scale datasets. However, developing a model for multi-tasking humanoid control faces significant challenges. Human kinematic data is available in open-source datasets for humanoid motion learning, but learni...
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| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2025-01-01
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| Seri Bilgileri: | IEEE Access |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/11000102/ |
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