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Shear-Primarily Based Grasp Control For Multi-fingered Underactuated Tactile Robotic Hands
Allan Mull edited this page 1 week ago
This paper presents a shear-based mostly management scheme for grasping and manipulating delicate objects with a Pisa/IIT anthropomorphic SoftHand geared up with delicate biomimetic tactile sensors on all five fingertips. These ‘microTac’ tactile sensors are miniature variations of the TacTip vision-primarily based tactile sensor, and can extract exact contact geometry and cordless power shears info at each fingertip for use as suggestions right into a controller to modulate the grasp while a held object is manipulated. Using a parallel processing pipeline, we asynchronously capture tactile photos and predict contact pose and force from multiple tactile sensors. Consistent pose and force models throughout all sensors are developed utilizing supervised deep studying with switch learning methods. We then develop a grasp management framework that makes use of contact drive suggestions from all fingertip sensors simultaneously, permitting the hand to safely handle delicate objects even underneath exterior disturbances. This management framework is applied to several grasp-manipulation experiments: first, retaining a versatile cup in a grasp with out crushing it underneath changes in object weight
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