论文标题
基于视觉和触觉测量的概率表面摩擦估计
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
论文作者
论文摘要
准确地对物体的局部表面特性进行建模对于从抓地到材料识别的许多机器人应用至关重要。但是，很难估计表面特性，因为对物体的视觉观察不能传达有关这些特性的足够信息。相反，触觉探索很耗时，因为它仅提供与对象探索部分相关的信息。在这项工作中，我们提出了一个联合视觉耐热对象模型，该模型可以通过利用视觉和触觉信息的相关性以及机器人臂有限的触觉探索来估算整个对象上的表面摩擦系数。我们通过证明其在一系列实际多物质对象上估算不同摩擦系数的能力来证明该方法的有效性。此外，我们说明了估计的摩擦系数如何通过指导抓紧计划者对高摩擦区域来提高成功率。
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.
