Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots
Mertan, Alican
Mertan, Alican
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Abstract
Soft robotics is a rapidly growing area that would benefit from design automation. A major hurdle limiting co-optimization is the fragile specialization between a robot's controller and the particular body plan it controls. Here we posit that modular controllers are more robust to changes to a robot’s body. Moreover, we show that the increased transferability enables more effective brain-body co-evolution, resulting in higher overall performance. We hope that this work helps provide specific methods to improve soft robot design automation in this particular setting, while also providing evidence to support our understanding of the challenges of brain-body co-optimization more generally.
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2023-01-01
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Graduate
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Computer Science
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Graduate College
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Engineering and Math Science