Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance. Project Website: https://sites.google.com/view/denoising-heat-inspired
@article{arxiv.2310.12609,
title = {Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning},
author = {Junwoo Chang and Hyunwoo Ryu and Jiwoo Kim and Soochul Yoo and Jongeun Choi and Joohwan Seo and Nikhil Prakash and Roberto Horowitz},
journal= {arXiv preprint arXiv:2310.12609},
year = {2024}
}