Max Robotics — Global Service Robot Directory + US Certification

About

Hee-Seung Moon is a researcher at the forefront of human-robot interaction, specializing in how robots can physically guide and collaborate with people. His work centers on developing robotic guide systems that use haptic (touch-based) feedback to communicate with human followers, creating a bidirectional channel where the robot not only leads but also senses and adapts to human reactions. Moon’s key contributions include pioneering the use of variational autoencoders to extract meaningful features from depth images of users following a robotic guide, enabling more natural and responsive human-robot coupling. His 2019 paper on this topic has garnered 12 citations, establishing a foundation for data-driven approaches in physical human-robot interaction. Moon further advanced the field by introducing a human path prediction network (HPPN) that allows robots to anticipate a user’s next movement, dramatically reducing the number of human trials needed for training—a critical step toward sample-efficient robot learning. His research has also explored how local-adaptive haptic guidance affects motor learning in path-following tasks, challenging long-held assumptions about the role of haptic feedback in skill acquisition. With a growing body of work cited across robotics and human factors engineering, Moon is shaping how robots can safely and intuitively guide people in real-world settings.