DiPPeST：基于扩散的路径规划器，用于四足机器人轨迹合成
DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots
AI总结：
DiPPeST是一种基于扩散的图像和目标条件轨迹生成器，通过零样本适配和实时局部路径细化，在四足机器人上实现高避障成功率，并在狭窄环境中优于现有局部规划器。
摘要：
我们提出了DiPPeST，一种新颖的基于图像和目标条件扩散的轨迹生成器，用于四足机器人路径规划。DiPPeST是我们先前引入的基于扩散的2D全局轨迹生成器（DiPPeR）的零样本适配。所引入的系统结合了一种新颖的局部实时路径细化策略，该策略对相机输入具有反应性，无需任何进一步训练、图像处理或环境解释技术。DiPPeST在标称环境中实现92%的避障成功率，在像素变化比DiPPeR复杂多达3.5倍的环境中测试时，平均成功率为88%。开发了一个视觉伺服框架以实现真实世界执行，在四足机器人上测试，在不同环境中实现80%的成功率，并在狭窄环境中展现出优于复杂的最先进局部规划器的改进行为。
英文摘要：
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.
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