Max Robotics — Global Service Robot Directory + US Certification

About

Clayton T. Morrison’s research lies at the compelling intersection of cognitive science, artificial intelligence, and human-robot interaction. He is best known for pioneering work on how humans naturally teach complex tasks to autonomous agents, aiming to replace rigid programming with intuitive, multi-modal instruction. His foundational paper, “An Image Schema Language” (2006, 23 citations), introduced a groundbreaking formalism for representing dynamic spatial and conceptual relationships, bridging symbolic AI with embodied cognition. Morrison’s most influential contributions, however, center on understanding the nuances of human teaching. His 2011 and 2013 papers, both titled “Towards Understanding How Humans Teach Robots” (totaling 38 citations), established critical frameworks for designing interfaces that accommodate natural instruction—combining demonstration, feedback, and dialogue. This work directly addresses the challenge of decoding human intention during teaching, a theme he explored further in his 2011 paper “Challenges to decoding the intention behind natural instruction” (9 citations). Through his leadership in the Epigenetic Robotics workshops and his empirical studies with simulated agents, Morrison has shaped how researchers think about making robots teachable by non-experts, laying essential groundwork for accessible, intuitive human-AI collaboration.