EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909832273985536 |
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| author | Li, Yixiao Barth, Julia Kiefer, Thomas Fraij, Ahmad |
| author_facet | Li, Yixiao Barth, Julia Kiefer, Thomas Fraij, Ahmad |
| contents | Multi-modal behavior cloning faces significant challenges due to mode averaging and mode collapse, where traditional models fail to capture diverse input-output mappings. This problem is critical in applications like robotics, where modeling multiple valid actions ensures both performance and safety. We propose EBGAN-MDN, a framework that integrates energy-based models, Mixture Density Networks (MDNs), and adversarial training. By leveraging a modified InfoNCE loss and an energy-enforced MDN loss, EBGAN-MDN effectively addresses these challenges. Experiments on synthetic and robotic benchmarks demonstrate superior performance, establishing EBGAN-MDN as a effective and efficient solution for multi-modal learning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning Li, Yixiao Barth, Julia Kiefer, Thomas Fraij, Ahmad Machine Learning Multi-modal behavior cloning faces significant challenges due to mode averaging and mode collapse, where traditional models fail to capture diverse input-output mappings. This problem is critical in applications like robotics, where modeling multiple valid actions ensures both performance and safety. We propose EBGAN-MDN, a framework that integrates energy-based models, Mixture Density Networks (MDNs), and adversarial training. By leveraging a modified InfoNCE loss and an energy-enforced MDN loss, EBGAN-MDN effectively addresses these challenges. Experiments on synthetic and robotic benchmarks demonstrate superior performance, establishing EBGAN-MDN as a effective and efficient solution for multi-modal learning tasks. |
| title | EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.07562 |