Baris Ulutas

Papers

Total Citations

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About

Baris Ulutas is a pioneering researcher in cognitive robotics, whose work bridges the gap between artificial intelligence and autonomous systems. His primary research areas include affordance learning, humanoid robot control, and neural network-based adaptive systems. Ulutas is best known for his groundbreaking work on cognitive robots that use internal rehearsal—a form of simulated practice—to learn general affordance relations from their experiences. This innovative approach, introduced in his most-cited paper (31 citations), models affordances as statistical relations between actions, object properties, and outcomes, enabling robots to predict and adapt to new situations without explicit programming. His second most-cited work (30 citations) addresses the design of a hybrid controller combining neural networks, PID control, and grey prediction for the humanoid robot ISAC, which uses pneumatically actuated soft arms. This work has significant implications for safe, compliant human-robot interaction. Though his citation counts are modest, Ulutas’s contributions are foundational in the subfield of cognitive robotics, particularly in enabling robots to learn from internal simulation rather than trial-and-error in the physical world. His research continues to influence the development of more autonomous, adaptive robotic systems.