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About

Connor Brooks is a robotics researcher whose work sits at the intersection of human-robot interaction, shared autonomy, and robot learning. His research addresses a fundamental challenge in modern robotics: making robots more intuitive, adaptable, and effective collaborators for human users across a range of skill levels and task contexts. Brooks's most influential contribution, "ARC-LfD" (2021, 33 citations), advances Learning from Demonstration by incorporating augmented reality to enable ongoing robot skill maintenance and adaptation — moving beyond one-time teaching to support long-term resilience in changing environments. His work on shared autonomy (2019, 21 citations) tackles the complexity of robotic teleoperation by balancing autonomous goal-directed behavior with active information gathering, improving operator experience without sacrificing control. Complementing this, his research on mental models (2019, 13 citations) explores how humans form beliefs about robot capabilities and intentions, providing a theoretical foundation for more transparent and trustworthy human-robot partnerships. Brooks also investigates proactive robot collaboration in unstructured tasks and the role of visualization in fostering user acceptance of assistive control systems. Together, his publications — accumulating over 80 citations — reflect a coherent research vision: empowering everyday users to work alongside robots more naturally, safely, and effectively.