@techreport {pub6182,
	title = {CCDP: Composition of Conditional Diffusion Policy for Interactive Sampling Refinement},
	author = {Amirreza Razmjoo AND Sylvain Calinon AND Michael Gienger AND Fan Zhang},
	year = {2025},
	month = {October},
	abstract = {Learning from demonstrations offers a promising approach in robotics by enabling systems to learn directly from data without requiring explicit models, simulations, or detailed task definitions. During inference, actions are sampled from the learned distribution and executed on the robot. However, sampled actions may fail for various reasons, and simply repeating the sampling step until a successful action is obtained can be inefficient. In this work, we propose an enhanced sampling strategy that refines the sampling distribution to avoid previously unsuccessful actions. Our approach yields a low-level controller that dynamically adjusts its sampling space to improve efficiency when prior samples fall short. We validate our method across several tasks, including door opening with unknown directions, object manipulation, and button-searching scenarios, demonstrating that our approach outperforms traditional baselines. },
	publisher = {arxiv},
	booktitle = {Arxiv}
}