視覚と触覚の計測に基づく表面摩擦の確率的推定
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
視覚情報と限られた触覚探索を組み合わせ、物体全体の摩擦係数を推定する視触覚統合モデルを提案し、把持計画の改善に有効であることを示した。
著者: Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka, Ville Kyrki
分類: cs.RO, cs.LG
原文アブストラクト
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic exploration is time consuming as it only provides information relevant to the explored parts of the object. In this work, we propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object by exploiting the correlation of visual and haptic information, together with a limited haptic exploration by a robotic arm. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects. Furthermore, we illustrate how the estimated friction coefficients can improve grasping success rate by guiding a grasp planner toward high friction areas.
