Yuqi Kong

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About

Yuqi Kong is a leading researcher in socially adaptive robotics and human-robot interaction, with a focus on developing intelligent navigation systems that prioritize pedestrian comfort and social compliance. Their work bridges the gap between autonomous mobile robots and natural human behavior by integrating advanced machine learning techniques into path planning. Kong’s most-cited paper, "Socially Adaptive Path Planning Based on Generative Adversarial Network" (2024, 4 citations), introduces a novel GAN-based framework that enables robots to anticipate and adapt to pedestrian movements, ensuring psychologically comfortable interactions. In earlier work, "NRTIRL Based NN-RRT* Path Planner in Human-Robot Interaction Environment" (2022, 4 citations), they pioneered a neural network-driven approach that combines inverse reinforcement learning with sampling-based planning to generate socially aware trajectories. These contributions are foundational for deploying robots in crowded public spaces, such as hospitals or shopping malls, where seamless coexistence with humans is critical. Kong’s research has been recognized for its practical impact on autonomous navigation, earning citations from peers in robotics and AI. Their work not only advances algorithmic efficiency but also sets new standards for ethical, human-centered robot design.