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arXiv · 2510.07562

EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning

Yixiao Li·Julia Barth·Thomas Kiefer·Ahmad Fraij
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Abstract

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.

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Yixiao Li, Julia Barth, Thomas Kiefer, Ahmad Fraij. 2025-10-08. EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning. https://arxiv.org/abs/2510.07562

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