Evolving Embodied Intelligence: Graph Neural Network--Driven Co-Design of Morphology and Control in Soft Robotics

📰 ArXiv cs.AI

Evolving embodied intelligence in soft robotics using graph neural networks for co-design of morphology and control

advanced Published 23 Mar 2026

Action Steps

Define the problem of co-designing morphology and control in soft robotics
Implement graph neural networks to model the interaction between body and brain
Use evolutionary algorithms to optimize morphology and control simultaneously
Evaluate the performance of the co-designed soft robots

Who Needs to Know This

Robotics engineers and AI researchers can benefit from this approach to develop more efficient and adaptive soft robots, as it allows for simultaneous optimization of morphology and control

Key Insight

💡 Graph neural networks can be used to co-design morphology and control in soft robots, enabling more efficient and adaptive robots

Key Takeaways

Evolving embodied intelligence in soft robotics using graph neural networks for co-design of morphology and control

Full Article

Title: Evolving Embodied Intelligence: Graph Neural Network--Driven Co-Design of Morphology and Control in Soft Robotics

Abstract:
arXiv:2603.19582v1 Announce Type: cross Abstract: The intelligent behavior of robots does not emerge solely from control systems, but from the tight coupling between body and brain, a principle known as embodied intelligence. Designing soft robots that leverage this interaction remains a significant challenge, particularly when morphology and control require simultaneous optimization. A significant obstacle in this co-design process is that morphological evolution can disrupt learned control str

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