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Joshua E. Auerbach is a leading researcher in evolutionary robotics and embodied artificial intelligence, whose work fundamentally explores how robot bodies and brains can be co-designed through artificial evolution. His major contributions center on developing methods to simultaneously evolve robot morphology (physical structure) and control systems, challenging the traditional approach of designing robots by hand. Auerbach pioneered the use of Compositional Pattern Producing Networks (CPPNs) as a generative encoding for creating complete robots, as demonstrated in his highly cited 2011 paper (46 citations) and his 2010 work on evolving three-dimensional physical structures (47 citations). His landmark 2017 proof-of-concept study (58 citations) demonstrated real robots capable of reproduction, pushing evolutionary robotics from simulation into the physical world. Auerbach is also the creator of RoboGen, an open-source software and hardware platform for robotics and AI education, which has been recognized for its impact on inquiry-based learning (45 citations). His research on the relationship between environmental complexity and morphological complexity (52 citations) has provided foundational insights into embodied cognition. With over 400 total citations across his most influential works, Auerbach's contributions continue to shape how researchers approach the automatic design of intelligent, physically-grounded agents.