让机器人通过模块化控制实现高效演化，支持新形态快速适配。
ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots
- 用可切换的专家模块控制不同身体结构，实现协同进化
- 预训练专家模块可直接接入，加速探索新形态设计空间
- 适合研究机器人自适应演化与模块化控制的学者
本文提出一种机器人演化与学习模型，联合优化潜空间设计向量（基因型）分布与由表型潜坐标门控的控制专家混合体（神经模块）。该方法避免为每个机器人单独训练策略（效率低）或使用统一控制器（行为保守），在两者之间取得平衡。不同身体结构可激活/去激活不同的感知运动回路组合，保留祖先知识的同时支持局部重构。部分控制器更新不影响其他专家模块的已有知识，且可直接嵌入预训练策略，引导演化进入包含理想形态特征的潜在空间区域。这一过程称为「evo by demo」，可用于引导自由演化朝向预定义的典型结构。视频与代码见：https://eco-moe.github.io。
原文摘要 · Abstract (English)
In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixture of control experts (neural modules), which are gated by the latent coordinates of each decoded design (phenotype). This provides a scalable alternative to co-design algorithms that either train an individual policy for every robot, which is inefficient, or a monolithic universal controller for all robots, which results in overly conservative structures and behaviors. Our approach lies somewhere between these two extremes, preserving ancestral knowledge in a unified yet modular framework in which different body plans activate and deactivate different combinations of learned sensorimotor circuits for goal-directed behavior. This allows one part of the controller to be overhauled to better suit new species of designs as they emerge without disrupting the hard-earned knowledge contained within other expert modules. It also allows pretrained expert policies to be directly plugged into the mixture, which can steer evolution into otherwise unexplored areas of latent space containing desired morphological traits. We refer to this process as "evo by demo" and explore how it may be used to guide freeform evolution toward canonical structures defined by the pretrained model. Videos and code can be found at: https://eco-moe.github.io.
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