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Diffusion models have risen as a powerful tool in robotics due to theirflexibility and multi-modality. While some of these methods effectively addresscomplex problems, they often depend heavily on inference-time obstacledetection and require additional equipment. Addressing these challenges, wepresent a method that, during inference time, simultaneously generates onlyreachable goals and plans motions that avoid obstacles, all from a singlevisual input. Central to our approach is the novel use of a collision-avoidingdiffusion kernel for training. Through evaluations against behavior-cloning andclassical diffusion models, our framework has proven its robustness. It isparticularly effective in multi-modal environments, navigating toward goals andavoiding unreachable ones blocked by obstacles, while ensuring collisionavoidance.

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