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.

翻译：扩散模型因其灵活性和多模态性已成为机器人领域的有力工具。尽管部分方法能有效解决复杂问题，但它们往往严重依赖推理时的障碍物检测，并需要额外设备。针对这些挑战，我们提出了一种方法，在推理过程中仅凭单一视觉输入即可同时生成可达目标并规划避障运动路径。该方法的核心创新在于采用了一种避免碰撞的扩散核进行训练。通过与行为克隆模型及经典扩散模型的对比评估，我们的框架证明了其鲁棒性。该方法在多模态环境中尤为有效，能引导路径趋向目标并避开被障碍物阻挡的不可达区域，同时确保全程无碰撞。