  We present DiPPeST, a novel image and goal conditioned diffusion-based
trajectory generator for quadrupedal robot path planning. DiPPeST is a
zero-shot adaptation of our previously introduced diffusion-based 2D global
trajectory generator (DiPPeR). The introduced system incorporates a novel
strategy for local real-time path refinements, that is reactive to camera
input, without requiring any further training, image processing, or environment
interpretation techniques. DiPPeST achieves 92% success rate in obstacle
avoidance for nominal environments and an average of 88% success rate when
tested in environments that are up to 3.5 times more complex in pixel variation
than DiPPeR. A visual-servoing framework is developed to allow for real-world
execution, tested on the quadruped robot, achieving 80% success rate in
different environments and showcasing improved behavior than complex
state-of-the-art local planners, in narrow environments.
