Evolutionary AI for Morphological and Locomotor Design of Resilient Exploration Robots

We present an AI-driven evolutionary framework that co-designs morphology and locomotion for compliant (soft) robots intended for extraterrestrial terrains. Using CPPN-NEAT with novelty-guided search, our approach explores diverse body plans and gaits across Earth, Martian, and lunar gravity. In an ablation against direct encodings, CPPN-based robots achieved 1.78× greater mean displacement under Earth gravity (14.3 cm vs. 8.0 cm), while also exhibiting higher morphology-diversity scores (0.87 vs. 0.41), indicating improved exploration of the design space. Novelty search further enriched behavior-space coverage and yielded robust gaits under low gravity. Results highlight that diversity-centric, generative encodings can surface unconventional yet effective designs often overlooked by purely fitness-driven optimization.