Morphogene：用基因式蓝图联合优化机器人形态与控制策略
Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
机器人形态和控制策略可以像基因一样绑在一起联合优化，arXiv 上这篇 GeCode 在 2D 和 3D 任务上跑赢了现有方法，做具身智能的可以看看。
形态—控制协同设计方法通常用两个独立网络分别处理身体结构和控制策略，只通过任务目标间接耦合。论文提出 Morphogene，一个紧凑的潜在蓝图，通过 AdaConcat 在肢体层面同时条件化形态生成与控制生成。基于该表示的 GeCode 框架把协同设计转化为在 Morphogene 空间中的探索，每个锚点对应一个局部设计区域。在多种 2D 和 3D 协同设计任务上，GeCode 的收敛速度和最终性能均超过现有 state-of-the-art 方法。
Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
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.