SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity
For legged robot designers, SurGE offers a more efficient and consistent co-design method for non-differentiable problems involving elastic elements.
SurGE introduces a surrogate gradient-guided evolution framework for co-designing legged robots with parallel elasticity, achieving 6x lower cross-seed standard deviation and 18% tighter population concentration than vanilla CMA-ES in simulation, and a 37.65% reduction in design objective on hardware.
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.