DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots
<|reference_start|>DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots: 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.<|reference_end|>
@article{stamatopoulou2024dippest:,
    title={DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories
  Applied on Quadruped Robots},
    author={Maria Stamatopoulou and Jianwei Liu and Dimitrios Kanoulas},
    journal={arXiv preprint arXiv:2405.19232},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19232},
    primaryClass={cs.RO}
}