[
    {
        "id": "osp-25641",
        "type": "article-journal",
        "title": "Inferring Soil Friction Angle from Robot Foot-Ground Force Histories: A Bayesian Inverse Approach to Proprioceptive Soil Sensing",
        "author": [
            {
                "family": "Xu",
                "given": "Dawei"
            },
            {
                "family": "Wang",
                "given": "Zhijie"
            }
        ],
        "URL": "https://omanscience.com/en/articles/inferring-soil-friction-angle-from-robot-foot-ground-force-histories-a-bayesian-inverse-approach-to-proprioceptive-soil-sensing",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]