发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一个无曲目软体机器人损伤恢复基准，采用九次在线试验，GP-EI控制器在75体队列中优于多种基线，并验证了损伤先验的附加值。
AI 中文摘要
我们提出了一个无曲目的软体机器人损伤恢复基准，在九次在线试验下进行评估。该协议将每个形态内的损伤掩码配对，保留一个测得的标称回退，并将方法开发与新身体上的评估分离。一个高斯过程期望改进（GP-EI）参考控制器使用三次初始化探测和六次反馈选择的滚动来调整执行器相位。在两个不相交的75体队列中，它在同等预算下优于随机搜索、Sobol、CEM和CMA-ES。GP-EI在69/75个确认体上改善了最差掩码增益，并超过这四个基线0.235-0.310的平均最差掩码奖励。一个TuRBO风格的局部GP是最接近的比较器；配对置信区间包含零。确认后的固定控制器重放发现平均恢复5.06个体素宽度，同时最差掩码p99几何边缘应变增加0.0031；一个严格的“无新增需求”部署门保留了48.7%的平均增益。在一个冻结的开发压力测试中，物理移除10%的占用体素，GP-EI改善了71/75个体，并超过官方BoTorch TuRBO 0.356的平均最差掩码增益。总之，可重放控制器、身体级推理和冻结队列评估为衡量学习损伤先验和未来适应方法的附加值提供了参考。
英文摘要
We present a repertoire-free benchmark for soft-robot damage recovery under nine online trials. The protocol pairs damage masks within each morphology, retains a measured nominal fallback, and separates method development from evaluation on new bodies. A Gaussian-process expected-improvement (GP-EI) reference controller adapts actuator phases using three initialization probes and six feedback-selected rollouts. Across two disjoint 75-body cohorts, it outperforms random search, Sobol, CEM, and CMA-ES under equal budgets. GP-EI improves the worst-mask gain on 69/75 confirmation bodies and exceeds these four baselines by 0.235-0.310 mean worst-mask reward. A TuRBO-style local GP is the closest comparator; the paired confidence interval includes zero. Post-confirmation fixed-controller replay finds 5.06 voxel widths of mean recovery together with a 0.0031 increase in worst-mask p99 geometric edge strain; a strict no-added-demand deployment gate retains 48.7% of mean gain. In a frozen development stress test that physically removes 10% of occupied voxels, GP-EI improves 71/75 bodies and exceeds official BoTorch TuRBO by 0.356 mean worst-mask gain. Together, the replayable controllers, body-level inference, and frozen-cohort evaluation provide a reference for measuring the added value of learned damage priors and future adaptation methods.
Comments8 pages, 7 figures, 3 tables
