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About

Gerard Canal is a leading researcher at the intersection of human-robot interaction (HRI) and AI planning, dedicated to making assistive robots truly adaptive and trustworthy. His core contributions lie in developing frameworks that enable robots to understand and respond to human preferences, particularly in physically assistive scenarios like dressing and feeding. Canal’s work on real-time gesture-based interaction systems (83 citations) and adaptive robotic feeding assistance (41 citations) has laid the groundwork for robots that can safely assist in daily living tasks outside controlled environments. A key achievement is his pioneering approach to joining high-level symbolic planning with low-level motion primitives, allowing robots to adapt their task plans to user preferences in real-time—as demonstrated in his influential work on assistive shoe dressing (44 citations). More recently, Canal has become a prominent voice in explainable robotics, investigating when and why users want explanations from robots (19 citations) and how to provide them for motion planning (22 citations). His innovative use of Large Language Models for adaptive plan generation (15 citations) marks a significant step toward robots that can understand natural language commands and user conditions, such as “I have back pain today,” to devise collaborative plans. With over 320 citations, Gerard Canal is shaping a future where assistive robots are not only capable but also considerate and transparent.