車輪付き二足歩行ロボットのためのデータ駆動型接触推定手法
A Data-driven Contact Estimation Method for Wheeled-Biped Robots
車輪付き二足歩行ロボットにおいて、トルク計測から学習したベイズフィルタと慣性計測による予測を組み合わせて接地状態を推定する手法を提案し、実機とシミュレーションで有効性を示した。
著者: Ü. Bora Gökbakan, Frederike Dümbgen, Stéphane Caron
分類: cs.RO, math.PR
原文アブストラクト
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.
