用代理梯度优化足式机器人弹性结构，提升设计效率与稳定性。
SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity
- 构建可微分的运动学动力学模型与控制策略，生成设计目标的代理梯度。
- 在4自由度跳跃机器人上，相比原始CMA-ES降低6倍种子间标准差，种群更集中。
- 硬件实验验证仿真结果，初始设计优化37.65%，适合非可微协同设计场景。
足式机器人与弹性元件的协同设计因接触动力学和机构耦合的不可微性而困难。本文提出SurGE框架，通过包含运动学动力学单刚体（Kino-SRB）模型和设计感知控制策略的可微分流程，计算设计目标的代理梯度，并以余弦退火步长衰减的均值移动方式注入CMA-ES。在具有单向并联弹簧的4自由度跳跃机器人设计空间中，SurGE实现跨种子标准差降低6倍、种群集中度提升18%，同时达到或优于最优目标值。2D设计子空间的硬件实验表明，从手工调优初始设计出发，SurGE使实际系统设计目标降低37.65%，且仿真中的改进趋势可稳定转移至物理系统。SurGE为足式机器人中不可微协同设计问题提供了加速潜力。
原文摘要 · Abstract (English)
Co-design of legged robots with elastic elements is challenging due to the non-differentiability of contact dynamics and mechanism engagement. This paper presents SurGE, a framework that computes surrogate gradients of the design objective through a differentiable pipeline consisting of a kinodynamic single-rigid-body (Kino-SRB) model and a design-aware control policy, and injects them into CMA-ES via mean shift with cosine-annealed step decay. On a 4-DOF design space of a hopping robot with unidirectional parallel spring, SurGE achieves 6 times lower cross-seed standard deviation and 18% tighter population concentration compared to vanilla CMA-ES, while matching or improving the best objective. Hardware experiments on a 2D design subspace show that, starting from a hand-tuned initial design, SurGE reduces the design objective by 37.65% on hardware, with the improvement trend identified in simulation transferring consistently to the physical system. SurGE provides the potential to accelerate non-differentiable co-design problems in legged robots via surrogate model gradients.
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