REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning

Researchers developed REFINE-DP, a hierarchical framework that combines diffusion policies with reinforcement learning to enable humanoid robots to perform complex loco-manipulation tasks. The system achieves over 90% success rate in simulation and demonstrates smooth autonomous execution in real-world environments for tasks like door traversal and object transport.