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About

Collin Cappelle is a researcher at the intersection of evolutionary robotics, artificial life, and embodied cognition. His work explores how robot morphology—the physical form and structure of a robot—can fundamentally shape its ability to evolve intelligent behavior and even ground abstract language. In his most-cited paper, "Simulating the evolution of soft and rigid-body robots" (15 citations), Cappelle developed accessible simulation tools that lower the barrier for new programmers to explore evolutionary robotics, moving beyond traditional C++ engines. His 2016 study on morphological modularity demonstrated that breaking a robot's body into modular components allows its behavioral evolution to scale linearly with environmental complexity—a significant insight for designing adaptable robots. Perhaps most creatively, in "Word2vec to behavior" (7 citations), Cappelle showed that a robot's physical form can help bridge the gap between neural word embeddings and real-world actions, suggesting that morphology itself facilitates the grounding of language in machines. This work points toward robots that can understand natural language commands not just through processing, but through their very bodies.