Shape-based object classification and recognition through continuum manipulation

Huitan Mao, Jing Xiao, Mabel M. Zhang, Kostas Daniilidis

Abstract: We introduce a novel approach to shape-based object classification and recognition through the use of a continuum manipulator. Noticing the fact that when a continuum manipulator wraps around an object in a whole-arm grasping, its own shape is indicative of the shape of the object, our approach enables learning and recognition of object classes based on the shapes of continuum wraps. It offers the following advantages: (1) recognition of objects that are not easily detected by vision, such as transparent objects, and (2) highly efficient recognition of such objects of varied sizes due to high-level and rich shape information in each wrap, unlike recognition based on tactile sensing via conventional grasping. Simulation and experiments demonstrate the effectiveness of our approach.