  In this work, we present DiPPeR, a novel and fast 2D path planning framework
for quadrupedal locomotion, leveraging diffusion-driven techniques. Our
contributions include a scalable dataset generator for map images and
corresponding trajectories, an image-conditioned diffusion planner for mobile
robots, and a training/inference pipeline employing CNNs. We validate our
approach in several mazes, as well as in real-world deployment scenarios on
Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 23
times faster for trajectory generation against both search based and data
driven path planning algorithms with an average of 87% consistency in producing
feasible paths of various length in maps of variable size, and obstacle
structure. Website: https://rpl-cs-ucl.github.io/DiPPeR
