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About

Mark Khazanov is a robotics researcher whose work sits at the intersection of soft robotics, morphological computation, and evolutionary algorithms. His most significant contributions center on tensegrity robots — lightweight, highly deformable structures composed of interconnected rods and cables — and their potential for real-world applications such as search-and-rescue missions and deployable field structures. Khazanov's landmark studies, "Exploiting Dynamical Complexity in a Physical Tensegrity Robot to Achieve Locomotion" (2013) and "Evolution of Locomotion on a Physical Tensegrity Robot" (2014), each garnering over 20 citations, demonstrated that the inherent mechanical complexity of tensegrity systems need not be a control burden but can instead be harnessed as a computational resource — a concept known as morphological computation. Rather than simplifying complex dynamics through traditional control strategies, Khazanov's approach embraces that complexity, using evolutionary algorithms to discover effective locomotion gaits. His 2014 work on controllable actuation further refined practical methods for directing tensegrity movement. Collectively, his research has helped establish tensegrity platforms as a serious contender in soft robotics, influencing how engineers think about the relationship between physical structure and intelligent behavior.