DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots

TL;DR

DiPPeST is a diffusion-based trajectory generator for quadruped robots that enables real-time, obstacle-aware path planning without additional training, achieving high success rates in complex environments.

Contribution

It introduces DiPPeST, a novel diffusion-based path planner with real-time local refinement, applicable to quadruped robots without extra training or environment interpretation.

Findings

92% obstacle avoidance success in nominal environments

88% success in highly complex environments

80% success rate in real-world robot experiments

Abstract

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.