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

• URL: http://arxiv.org/abs/2310.12609v4
• Date: Mon, 12 Feb 2024 07:50:24 GMT
• Title: Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
• Authors: Junwoo Chang, Hyunwoo Ryu, Jiwoo Kim, Soochul Yoo, Jongeun Choi, Joohwan Seo, Nikhil Prakash, Roberto Horowitz
• Abstract summary: Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. We present a method that simultaneously generates only reachable goals and plans motions that avoid obstacles.
• Score: 3.074694788117593
• License: http://creativecommons.org/licenses/by/4.0/
• 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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