发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基因驱动的Morphogene表示与GeCode算法，通过紧凑潜在空间联合优化智能体形态与控制，实现高效协同设计，在2D和3D任务中超越现有方法。
AI 中文摘要
形态-控制协同设计将智能体的身体结构与控制策略作为一个整体具身系统进行联合优化。然而，现有方法通常使用仅通过共享任务目标间接耦合的独立网络来建模形态设计与控制，限制了显式的高层协调。受协调生物发育的自然基因启发，我们提出了Morphogene，一种紧凑的潜在蓝图，用于连接智能体的身体与大脑。通过AdaConcat，Morphogene在肢体层面联合条件化形态与控制的生成，使其变化能够诱导两个组件的协同改变。基于这一表示，我们提出了GeCode，将协同设计表述为在紧凑的Morphogene空间中的探索。每个Morphogene锚定一个局部设计区域，在该区域内探索邻近的身体-大脑设计，而性能引导的更新将这些锚点移向有前景的区域，以在更广泛的设计空间中进行更高效的探索。该过程结合了局部细化与全局探索，同时保持身体-大脑兼容性。在多种2D和3D协同设计任务上的大量实验表明，GeCode始终优于现有最先进方法，实现了显著更快的收敛速度和更高的最终性能。
英文摘要
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.
