Max Robotics — Global Service Robot Directory + US Certification

About

M. J. Drinkwater’s research lies at the critical intersection of human-robot interaction and artificial intelligence, focusing on the foundational challenge of trust in human-robot teams. Their central contribution is the development of a novel “inverse trust metric”—a computational framework that enables robots to estimate how much a human teammate trusts them. Rather than merely reacting to commands, Drinkwater’s work empowers robots to proactively adapt their behavior based on this trust assessment, using case-based reasoning to learn from past interactions. This approach directly addresses the problem of robot underutilization in teams, where a capable robot may be ignored if trust is low. Across a series of highly cited papers (2014–2016), including “How Much Do You Trust Me?” and “Trust-guided behavior adaptation,” Drinkwater has systematically shown how robots can adjust their actions to build and maintain trust, even incorporating operator feedback to refine their behavior. With over 50 citations across these core works, Drinkwater’s research provides a practical, data-driven pathway for integrating autonomous agents into human teams, making them more effective and accepted partners in high-stakes environments.