Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning
CoRR(2023)
摘要
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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关键词
diffusion,collision free
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