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About

Mark Zolotas is a robotics researcher whose work spans human-robot interaction, explainable AI, shared control systems, and autonomous mobile robotics. He is perhaps best known for pioneering the use of augmented reality to make robotic assistance more transparent and interpretable — a challenge he tackled through his highly cited work on head-mounted AR displays for robotic wheelchairs, which earned 47 and 33 citations respectively and established him as a leading voice in explainable shared control. His research recognizes that effective human-robot collaboration hinges on users forming accurate mental models of robot behavior, and he has consistently developed tools to bridge that cognitive gap across domains including remote manipulation and assistive mobility. Beyond explainability, Zolotas has made meaningful contributions to off-road autonomous navigation, applying Gaussian Process Regression to model the complex dynamics of skid-steer robots operating at their physical limits. His work also extends to ergonomic human-robot collaboration, exploring how robots can promote healthier worker postures without fostering over-reliance. Diverse projects like instruMentor, an interactive musical tutoring robot, reveal the breadth of his interests. With a growing citation record and contributions spanning perception, control, and human factors, Zolotas represents an emerging researcher with significant real-world impact across multiple robotics domains.