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arXiv · 2310.12609

Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning

Junwoo Chang·Hyunwoo Ryu·Jiwoo Kim·Soochul Yoo·Jongeun Choi·Joohwan Seo·Nikhil Prakash·Roberto Horowitz
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Abstract

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

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Junwoo Chang, Hyunwoo Ryu, Jiwoo Kim, Soochul Yoo, Jongeun Choi, Joohwan Seo, Nikhil Prakash, Roberto Horowitz. 2023-10-19. Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning. https://arxiv.org/abs/2310.12609

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