Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
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Abstract (arXiv)
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 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 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 25 Sep 2026.
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32 of 47 references.
References · 32
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- 2017Scalable Co-Optimization of Morphology and Control in Embodied Machinescited by 38
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20 more
- 2019Neural Graph Evolution: Towards Efficient Automatic Robot Designcited by 34
- 2021Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Designcited by 34
- 2023Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Responsecited by 31
- 2023Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Modelscited by 30
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- 2021Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platformscited by 22
- 2023Universal Morphology Control via Contextual Modulationcited by 21
- 2023Efficient automatic design of robotscited by 11
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- 2025BodyGen: Advancing Towards Efficient Embodiment Co-Designcited by 9
- 2024RoboMorph: Evolving Robot Morphology using Large Language Modelscited by 8
- 2025DivControl: Knowledge Diversion for Controllable Image Generationcited by 7
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- 2025Convergent Functions, Divergent Formscited by 4
- 2025House of Dextra: Cross-embodied Co-design for Dexterous Handscited by 4
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- 2026Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimizationcited by 2
- 2025Knowledge Diversion for Efficient Morphology Control and Policy Transfercited by 1
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