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About

Nils Wantia is a leading researcher in cognitive robotics and human-robot collaboration, with a focus on enabling robots to learn and execute complex assembly tasks. His work bridges the gap between low-level sensorimotor control and high-level symbolic planning, particularly through learning by demonstration and active learning. In his most-cited paper (64 citations), Wantia introduced a three-level cognitive system that allows robots to transfer skills between the sensorimotor and planning domains, a key contribution to making industrial robots more adaptable. He further advanced the field with systems for active learning of manipulation sequences (23 citations), where robots autonomously explore and request instruction to maximize learning progress. Wantia’s research has direct industrial impact, as seen in his work on human-robot cooperative processes and task planning for hybrid work cells, which aim to reduce the complexity and cost of deploying robots in small and medium-sized enterprises. He has also pioneered the use of Virtual Reality and Digital Twins for grounding machine learning and validating assembly processes, notably in the EU-funded projects IntellAct and ReconCell. With a career spanning foundational cognitive architectures and practical industrial applications, Wantia’s work is essential reading for anyone interested in making robots truly collaborative and intelligent partners on the factory floor.