Determining object properties from tactile events during grasp failure

Determining object properties from tactile events during grasp failure

Kwiatkowski, Jennifer and Lavertu, Jean Simon and Gourrat, Chloe and Duchaine, Vincent

IEEE International Conference on Automation Science and Engineering 2019

Abstract : Robotic grasp planners are unable to successfully grasp objects 100% of the time. During the failures, inferring a better understanding of the in-hand item could lead to more robust regrasp strategies. This paper explores how relevant object parameters, such as its surface properties and its weight distribution, may be extracted from a time series of tactile feedback generated during a grasp failure. The surface texture of four known objects was classified with an accuracy of 90.22%. Objects whose weight distribution were top heavy, bottom heavy, or evenly distributed were distinguished with an accuracy of 86.9%. In both cases, only a small dataset was required to achieve a relatively high performance.