A Data-driven Contact Estimation Method for Wheeled-Biped Robots

Abstract¶

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.

Content¶

Pre-print

BibTeX¶

@unpublished{gokbakan2024,
  title = {{A Data-driven Contact Estimation Method for Wheeled-Biped Robots}},
  author = {G{\"o}kbakan, {\"U}. Bora and D{\"u}mbgen, Frederike and Caron, St{\'e}phane},
  url = {https://hal.science/hal-04726386},
  year = {2024},
  month = Oct,
}