Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically rely on gate-cycle priors or designated contact sensors. We design a contact estimator that is suitable for the emerging wheeled-biped robot types that do not have these features. To this end, we propose a Bayes filter in which update steps are learned from real-robot torque measurements while prediction steps rely on inertial measurements. We evaluate this approach in extensive real-robot and simulation experiments. Our method achieves better performance while being considerably more sample efficient than a comparable deep-learning baseline.
@article{arxiv.2410.12345,
title = {A Data-driven Contact Estimation Method for Wheeled-Biped Robots},
author = {Ü. Bora Gökbakan and Frederike Dümbgen and Stéphane Caron},
journal= {arXiv preprint arXiv:2410.12345},
year = {2025}
}