Morphogene：用基因式蓝图联合优化机器人形态与控制策略

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.

本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Morphogene、具身智能、形态控制协同设计、机器人，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Fu Feng, Ruixiao Shi, Yucheng Xie, Jing Wang, Xin Geng），经 AITOP 多维评分管线结构化提炼。