Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
This work addresses motion planning in robotics by reducing dependency on additional equipment, though it appears incremental as it builds on existing diffusion models with a novel kernel.
The paper tackles the problem of generating collision-free motion plans from visual inputs without relying on inference-time obstacle detection, achieving robust performance in multi-modal environments by simultaneously generating reachable goals and planning motions.
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