Why Morphology and Control Must Be Designed Together
Another high-impact trend gaining traction in 2025 is body-control co-design — the idea that robot morphology (the physical body) and control strategies (the brain) should be developed together rather than independently.
Traditional humanoid robots are often engineered with fixed body designs, with researchers focusing primarily on control algorithms afterward. However, this siloed approach limits adaptability and performance, especially in dynamic, real-world situations.
Body-control co-design draws inspiration from biological systems where form and control evolve together. By optimizing a robot’s body and control policies jointly, researchers aim to produce systems capable of greater agility, resilience, and task flexibility.
Embracing Evolution: A Call for Body-Control Co-Design in Embodied Humanoid Robots
This foundational position paper advocates for co-design methods that integrate morphology adaptation with control policy development. It argues that iteratively considering both body and control from the start can unlock new levels of performance and robustness.
Evolutionary Continuous Adaptive RL-Powered Co-Design for Humanoid Chin-Up Performance
This work demonstrates how evolutionary strategies combined with reinforcement learning can optimize hardware and control simultaneously for dynamic tasks. The results show that co-design approaches can significantly improve humanoid performance in complex, physically demanding skills.
Joint design methodologies challenge the traditional view that hardware must be set before control algorithms are developed. By iteratively adapting both together, researchers can produce more capable, robust humanoid agents, particularly for tasks requiring balance recovery, dynamic locomotion, and manipulative dexterity.
This approach is gaining momentum because it addresses fundamental limitations in current robotics engineering, pointing toward a future where morphology and intelligence co-evolve. It’s a critical step toward truly autonomous humanoid systems capable of handling real-world complexity.
