Design of an Optoelectronically Innervated Gripper for Rigid-Soft Interactive Grasping
This research aims to improve the robustness and accuracy of robotic grasping for manipulation tasks by addressing the deformation issues inherent in soft grippers.
This paper addresses the challenge of inaccurate grasping by soft grippers due to deformation. They developed an omni-directional adaptive soft finger with embedded optical fibers for deformation sensing, combined with machine learning to interpret light intensities. This led to a rigid-soft interactive grasping policy that improved grasping robustness through active adaptation and provided useful tactile information for subsequent manipulation tasks.
Over the past few decades, efforts have been made towards robust robotic grasping, and therefore dexterous manipulation. The soft gripper has shown their potential in robust grasping due to their inherent properties-low, control complexity, and high adaptability. However, the deformation of the soft gripper when interacting with objects bring inaccuracy of grasped objects, which causes instability for robust grasping and further manipulation. In this paper, we present an omni-directional adaptive soft finger that can sense deformation based on embedded optical fibers and the application of machine learning methods to interpret transmitted light intensities. Furthermore, to use tactile information provided by a soft finger, we design a low-cost and multi degrees of freedom gripper to conform to the shape of objects actively and optimize grasping policy, which is called Rigid-Soft Interactive Grasping. Two main advantages of this grasping policy are provided: one is that a more robust grasping could be achieved through an active adaptation; the other is that the tactile information collected could be helpful for further manipulation.