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About

Jack Kolb is an emerging researcher at the intersection of human-robot interaction, artificial intelligence, and autonomous systems, with a particular focus on human-robot teaming and cognitive modeling. His work addresses one of the most pressing challenges in collaborative robotics: accounting for the rich variability in human performance when designing effective mixed human-robot teams. Kolb's most recognized contributions center on predicting and leveraging individual human capabilities within teaming contexts. His research on cognitive states and situation awareness introduces frameworks for dynamically inferring a human teammate's mental model in real time, enabling robots to serve as more responsive and adaptive partners. He has also explored the downstream consequences of imperfect decision-support systems, examining how inaccurate robotic advisors affect shared decision-making in command-and-control scenarios — a practically crucial question as autonomous systems proliferate in high-stakes domains. Beyond teaming dynamics, Kolb has contributed to understanding human comfort in close-proximity robot interaction and applied constrained reinforcement learning to dexterous manipulation, broadening his methodological range. Though early in his career — his cited works spanning 2021 to 2024 — his growing body of work signals a researcher committed to making human-robot collaboration safer, smarter, and more human-centered.