Robust and flexible leader-following is a critical capability for robots to integrate into human society. While existing methods struggle to generalize to leaders of arbitrary form and often fail when the leader temporarily leaves the robot's field of view, this work introduces a unified framework addressing both challenges. First, traditional detection models are replaced with a segmentation model, allowing the leader to be anything. To enhance recognition robustness, a distance frame buffer is implemented that stores leader embeddings at multiple distances, accounting for the unique characteristics of leader-following tasks. Second, a goal-aware adaptation mechanism is designed to govern robot planning states based on the leader's visibility and motion, complemented by a graph-based planner that generates candidate trajectories for each state, ensuring efficient following with obstacle avoidance. Simulations and real-world experiments with a legged robot follower and various leaders (human, ground robot, UAV, legged robot, stop sign) in both indoor and outdoor environments show competitive improvements in follow success rate, reduced visual loss duration, lower collision rate, and decreased leader-follower distance.

翻译：稳健且灵活的领导者跟随是机器人融入人类社会的一项关键能力。现有方法难以泛化至任意形态的领导者，且在领导者暂时离开机器人视野时经常失效。本文提出一个统一框架以应对上述两个挑战。首先，用分割模型替代传统检测模型，使领导者可以是任意物体。为增强识别鲁棒性，设计了一种距离帧缓冲器，在不同距离下存储领导者嵌入特征，从而应对领导者跟随任务的独特特性。其次，构建了一种目标感知适应机制，根据领导者的可见性与运动状态调控机器人规划状态，并辅以基于图的规划器为每个状态生成候选轨迹，确保在避障的同时实现高效跟随。仿真实验与真实场景实验（以四足机器人为跟随者，领导者为人类、地面机器人、无人机、四足机器人及停止标志）在室内外环境中均表明，该方法在跟随成功率、视觉丢失时长、碰撞率及领导者-跟随者距离方面均有显著提升。