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

Описание: Venue: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Authors: Maria Stamatopoulou*, Jianwei Liu*, Dimitrios Kanoulas
equal contribution

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.

PDF: https://arxiv.org/abs/2405.19232
Project page: https://rpl-cs-ucl.github.io/DiPPeSTweb/
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