Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design

2026-09-25

AI Summary

This study addresses the limitation of existing morphology-control co-design methods, which suffer from decoupled body-brain modeling, indirect coupling, and the absence of explicit high-level coordination mechanisms. Inspired by biological genetics, this work proposes Morphogene, a compact latent blueprint, alongside the GeCode algorithm. By leveraging an AdaConcat technique to jointly generate morphologies and control policies, the approach achieves limb-level direct conditioning and coordinated evolution. Furthermore, it combines local refinement with performance-guided global anchor exploration to efficiently optimize within a compact search space. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art approaches across diverse 2D and 3D tasks, achieving both faster convergence rates and superior final performance.

Abstract

Morphology--control co-design jointly optimizes an agent's body structure and control policy as an integrated embodied system. However, existing methods typically model morphology design and control with separate networks coupled only indirectly through a shared task objective, limiting explicit high-level coordination. Inspired by natural genes that coordinate biological development, we introduce \textbf{Morphogene}, a compact latent blueprint that bridges an agent's body and brain. Through AdaConcat, Morphogene jointly conditions morphology and control generation at the limb level, allowing its variations to induce coordinated changes in both components. Building on this representation, we propose \textbf{GeCode}, which formulates co-design as exploration in the compact Morphogene space. Each Morphogene anchors a local design region in which nearby body--brain designs are explored, while performance-guided updates move these anchors toward promising regions for more efficient exploration of the broader design space. This process combines local refinement with global exploration while preserving body--brain compatibility. Extensive experiments across diverse 2D and 3D co-design tasks demonstrate that GeCode consistently outperforms existing state-of-the-art methods, achieving substantially faster convergence and higher final performance.

Problem

• morphology-control co-design
• embodied AI
• joint optimization
• body-brain coordination

Innovation

• Morphology-control co-design
• Morphogene
• AdaConcat
• GeCode
• Latent blueprint