Scientifically Interesting Morphology

Scientifically interesting morphology research focuses on understanding how the physical structure of systems, from robots to galaxies, influences their function and evolution. Current research employs diverse approaches, including evolutionary algorithms coupled with differentiable simulations for robot design, machine learning models like encoder-decoders and neural networks for morphology prediction and generation in various domains (e.g., neurons, galaxies, pathology images), and quality diversity algorithms for creating controllers adaptable to multiple morphologies. These studies contribute to a deeper understanding of complex systems by revealing the interplay between form and function, with implications for robotics, neuroscience, astronomy, and medical image analysis.