Slip in Bimanual Gripping of Deformable Objects with Gelsight Hybrid Adhesion
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Punamiya, Rohan Sundeep
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Abstract
Robotic object manipulation has increased exponentially over the last couple of decades. Detection and prevention of slip of objects plays a vital role in secure object grasping and manipulation. Through the sensory feedback provided by their skin, humans possess the remarkable ability to readily perceive slip. To attain a level of skill comparable to humans, robots must be equipped with artificial tactile sensing integrated into their system. In this work, object manipulation is studied within the context of Agility Robotics’ humanoid Digit robot. A custom mechanical bimanual gripper is designed to grip deformable objects with optical tactile Gelsight sensors equipped on each finger. The fabrication process is discussed in depth, along with the inverse kinematics model used to control gripper motion. After construction of the gripper, the problem of slip detection is decomposed into a classification problem by using the input from Gelsight sensors. The benefits and limitations of this novel design is discussed with future work on dynamic slip proposed.
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Undergraduate Research Option Thesis