进化具身智能：基于图神经网络的软机器人形态与控制协同设计
Evolving Embodied Intelligence: Graph Neural Network--Driven Co-Design of Morphology and Control in Soft Robotics
AI总结：
本文提出基于图神经网络的软机器人形态与控制协同设计方法，通过图结构建模实现形态演化与控制策略的动态适应，实验表明该方法在最终适应度和形态变化适应性上优于传统MLP方法。
摘要：
机器人智能行为并非仅源于控制系统，而是源于身体与大脑的紧密耦合，即具身智能原则。设计利用这种交互的软机器人仍是一项重大挑战，尤其当形态和控制需要同时优化时。在协同设计过程中，形态进化可能破坏已学习的控制策略，使现有知识难以复用或适应。我们通过开发基于图神经网络的协同设计方法来解决这一问题。每个机器人表示为图，图注意力网络（GAT）编码节点特征，池化表示通过多层感知机（MLP）头生成执行器命令或值估计。在进化过程中，继承遵循拓扑一致映射：共享GAT层被重用，MLP隐藏层被完整转移，匹配的执行器输出被复制，不匹配的则随机初始化并微调。这种形态感知的策略类使控制器在身体突变时能够适应。在基准测试中，我们的基于GAT的方法在最终适应度和对形态变化的适应性上优于传统仅使用MLP的协同设计方法。这些结果表明，图结构策略提供了更有效的接口，使进化形态与控制在具身智能中更加有效。
英文摘要：
The intelligent behavior of robots does not emerge solely from control systems, but from the tight coupling between body and brain, a principle known as embodied intelligence. Designing soft robots that leverage this interaction remains a significant challenge, particularly when morphology and control require simultaneous optimization. A significant obstacle in this co-design process is that morphological evolution can disrupt learned control strategies, making it difficult to reuse or adapt existing knowledge. We address this by develop a Graph Neural Network-based approach for the co-design of morphology and controller. Each robot is represented as a graph, with a graph attention network (GAT) encoding node features and a pooled representation passed through a multilayer perceptron (MLP) head to produce actuator commands or value estimates. During evolution, inheritance follows a topology-consistent mapping: shared GAT layers are reused, MLP hidden layers are transferred intact, matched actuator outputs are copied, and unmatched ones are randomly initialized and fine-tuned. This morphology-aware policy class lets the controller adapt when the body mutates. On the benchmark, our GAT-based approach achieves higher final fitness and stronger adaptability to morphological variations compared to traditional MLP-only co-design methods. These results indicate that graph-structured policies provide a more effective interface between evolving morphologies and control for embodied intelligence.
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