让肌肉机器人自动进化出最佳形态，提升复杂地形行走能力。
Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion
- 同时优化肌肉力量、速度和刚度，构建完整形态演化空间。
- 在四种地形上训练效率提升，运动更稳定。
- 用谱方法降维，高效探索庞大参数空间，适合机器人设计者。
肌骨骼机器人具备内在柔顺性与灵活性，是实现多样化运动的有前景范式。然而，现有研究多采用肌肉生理参数固定的模型，静态物理设定难以适应复杂任务的动态需求，限制了机器人性能上限。本文聚焦肌骨骼系统的形态与控制协同设计，不同于以往仅优化单一生理属性（如刚度）的研究，提出完整的肌骨骼形态演化空间，同步演化肌肉力量、速度与刚度。为应对由此带来的探索空间指数级膨胀问题，提出谱设计演化（SDE）框架，通过结合双侧对称先验与主成分分析（PCA），将复杂肌肉参数投影至低维谱流形，实现高效形态探索。在 MyoSuite 框架上对四类任务（步行、爬楼梯、丘陵地形、崎岖地形）进行评估，本方法在学习效率与运动稳定性方面均优于固定形态及标准进化基线。
原文摘要 · Abstract (English)
Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models with fixed muscle physiological parameters. This static physical setting fails to accommodate the diverse dynamic demands of complex tasks, inherently limiting the robot's performance upper bound. In this work, we focus on the morphology and control co-design of musculoskeletal systems. Unlike previous studies that optimize single physiological attributes such as stiffness, we introduce a Complete Musculoskeletal Morphological Evolution Space that simultaneously evolves muscle strength, velocity, and stiffness. To overcome the exponential expansion of the exploration space caused by this comprehensive evolution, we propose Spectral Design Evolution (SDE), a high-efficiency co-optimization framework. By integrating a bilateral symmetry prior with Principal Component Analysis (PCA), SDE projects complex muscle parameters onto a low-dimensional spectral manifold, enabling efficient morphological exploration. Evaluated on the MyoSuite framework across four tasks (Walk, Stair, Hilly, and Rough terrains), our method demonstrates superior learning efficiency and locomotion stability compared to fixed-morphology and standard evolutionary baselines.
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