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About

Pengfei Yi is a researcher advancing the frontiers of human-robot interaction (HRI), with a focus on creating safer, more intuitive, and efficient collaborative systems. Their work addresses critical challenges in how robots perceive and respond to human partners, moving beyond static, camera-dependent frameworks. Yi’s key contributions include developing a novel HRI method that combines human pose estimation with motion intention recognition, enabling robots to anticipate actions without relying on limited depth cameras—a paper that has garnered 34 citations. They have also pioneered asymmetric anomaly detection for HRI, a safety-critical approach that rapidly identifies abnormal events to prevent accidents during interaction. Further innovations include a gaze-point-driven HRI framework for single-person scenarios, efficient monocular distance detection for grasp and handover tasks, and a movement-supported HRI framework for bipedal humanoid robots that accounts for lower-limb locomotion. Most recently, Yi has explored trust-based active interaction strategies for human-robot collaboration, aiming to enhance cooperation efficiency by dynamically adapting to human trust levels. With a growing body of work spanning from 2021 to 2025, Pengfei Yi is shaping the future of responsive, safe, and adaptive robotic partners.