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

Understand

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.