发表机构
Washington State University（华盛顿州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过贝叶斯反演从机器人足-地力历史推断无粘性土壤内摩擦角，利用物质点法模型和高斯过程代理，在匹配模型实验中实现高精度识别，支持本体感觉土壤传感发展。
AI 中文摘要
四足机器人记录的足-地交互信号可能实现土壤强度的空间分布式原位表征。作为第一步，我们测试了能否从简化旋转腿的力历史中识别出无粘性土壤的内摩擦角$\phi$。采用物质点法实现的二维连续介质模型，以实测旋转腿力历史为基准，生成训练数据，两个高斯过程代理模型支持全历史的贝叶斯反演。在匹配模型实验中，该框架恢复了14个非网格内摩擦角，中位绝对误差约为$0.1^\circ$（最大约$0.7^\circ$）；报告的可信区间在所有情况下均包含真实值。这些结果确立了当正演模型正确指定时$\phi$是可识别的，并支持进一步开发用于空间变化地形的本体感觉土壤传感，应用范围从用于机器人训练的物理基础世界模型到火灾后边坡评估。
英文摘要
Foot-ground interaction signals recorded by quadruped robots may enable spatially distributed, in situ characterization of soil strength. As a first step, we test whether the internal friction angle $ϕ$ of cohesionless soil can be identified from the force history of a simplified rotating leg. A two-dimensional continuum model implemented with the material point method, benchmarked against measured rotating-leg force histories, generates the training data, and two Gaussian-process surrogates support Bayesian inversion of the full histories. In matched-model experiments, the framework recovers 14 off-grid friction angles with a median absolute error of approximately $0.1^\circ$ (maximum $\sim 0.7^\circ$); the reported credible intervals contain the true value in every case. These results establish that $ϕ$ is identifiable when the forward model is correctly specified, and support further development of proprioceptive soil sensing for spatially variable terrain, with applications from physics-grounded world models for robot training to post-wildfire slope assessment.
Comments14 pages, 12 figures
