Tactile object recognition in early phases of grasping using underactuated robotic hands

Identifying objects during the early phases of robotic grasping in unstructured environments is a crucial step toward successful dexterous robotic manipulation. Underactuated hands are versatile and quickly conform to unknown object surfaces to ensure a firm grasp. The trade-off of using such hands is that extracting information and recognizing objects is challenging due to the uncertainty introduced by the hand's flexibility and unexpected object movements under manipulation. Combining tactile sensors and machine learning models can provide valuable information about manipulated objects to overcome such drawbacks. The present paper explores tactile object identification under two situations: single grasp, analogous to the haptic glance in humans, and through brief exploratory procedures where a robotic thumb displaces the grasped object to excite the sensors. In both scenarios, a fuzzy controller ensures that data collection occurs under approximately the same conditions in terms of forces and vibrations. Machine learning methods used for the single-grasp and short-exploratory data confirm that the former can improve object recognition.

Automated summary

Identifying objects is a crucial step in robotic manipulation, and combining tactile sensors and machine learning models can help overcome the challenges posed by flexible robotic hands and unexpected object movements. This study explores tactile object identification through single grasping and brief exploratory procedures, demonstrating that single grasping can improve object recognition.