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About

Simon Haller is a leading researcher in cognitive robotics, whose work bridges ecological psychology and autonomous manipulation. His primary research areas include affordance-based robotics, action representation, and learning by demonstration. Haller’s most influential contribution is his seminal 2017 paper, “Computational models of affordance in robotics,” which has garnered 81 citations and provides a foundational taxonomy for applying J. J. Gibson’s ecological concept of affordances to robotic systems. This work systematically classifies how robots can perceive and act upon action possibilities in their environment. He further advanced the field with his 2019 taxonomy of action representations (18 citations), clarifying how robots can define and execute robust manipulation skills. Haller’s practical impact is demonstrated through his development of a three-level cognitive system for teaching robots assembly tasks (64 citations), enabling learning and transfer across sensorimotor and planning levels. His 2014 work on active learning of manipulation sequences (23 citations) introduced a system that allows robots to learn goal-directed tasks through a mix of exploration and instruction, actively maximizing learning progress. Through these contributions, Haller has established himself as a key figure in creating more intuitive, adaptable robotic systems capable of learning complex manipulation skills.