Max Robotics — Global Service Robot Directory + US Certification

About

Hebert Azevedo-Sa is a leading researcher at the intersection of human-robot interaction and trust in automation, with a focus on designing collaborative systems that adapt to human behavior. His work centers on developing computational models of trust—both natural and artificial—to enable more effective human-robot teams. Azevedo-Sa’s major contributions include a novel task allocation method that leverages artificial trust to assign tasks based on each agent’s unique capabilities, improving team performance in heterogeneous human-robot teams. He also proposed a unified bi-directional model for trust, bridging the gap between human trust in robots and robots’ trust in humans. His most-cited paper, from the 2021 ACM/IEEE International Conference on Human-Robot Interaction (29 citations), explores how advances in perception and AI can lead to seamless interaction, while his 2022 work on heterogeneous task allocation (25 citations) has been influential in the field. Azevedo-Sa’s research has direct applications in automated driving, where he addresses trust calibration for safer collaboration between drivers and autonomous vehicles. His work is essential reading for anyone interested in building trustworthy, adaptive robotic systems.