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arXiv Machine Learning By Fu Feng, Ruixiao Shi, Yucheng Xie, Jing Wang, Xin Geng

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

Read the original on arXiv Machine Learning →

The paper introduces Morphogene, a compact latent blueprint that links an agent’s body and control policy, enabling coordinated changes at the limb level through AdaConcat. Building on this, GeCode treats co-design as exploration within Morphogene space, using local refinement and global exploration to efficiently navigate design regions. Experiments on various 2D and 3D tasks show that GeCode outperforms state‑of‑the‑art methods, achieving faster convergence and higher performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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