Prior-Constrained Explorative Guidance for Generalization in Diffusion Motion Planning

Abstract

Diffusion-based planners have achieved generalization comparable to classical planners by leveraging inference-time optimization through guidance. However, their limited ability to capture environmental variations often constrains their responsiveness in unseen settings. In addition, the diversity-consistency trade-off inherent in guidance has remained unresolved. In this work, we propose Prior-Constrained Explorative Guidance (PCEG), a novel approach that gathers environmental information through local exploration and prevents guided samples from converging prematurely to similar solutions by leveraging a trajectory prior. The collected information is included in the guidance via stochastic gradient estimation, while a succinct parameter scheduling strategy enables latent optimization driven by environmental signals without significant computational overhead. Furthermore, during the modal-seeking stages of the reverse diffusion process, we employ a Gaussian Process (GP) to enforce dynamics-informed priors, effectively constraining the exploration region of each sample and thereby enhancing solution diversity. Across diverse benchmarks including 7-degree-of-freedom (7-DoF) robot-arm manipulation, PCEG substantially improves success rate by up to 30 percentage points compared to competitive diffusion planners without compromising trajectory quality, even in scenarios involving unseen obstacles. Real-world experiments further validate these findings, showcasing the generation of smooth, collision-free trajectories in novel environments. The project page is available at https://rml-unist.github.io/PCEG/.