Learning-based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping
This work addresses grasping challenges in robotics by enabling adaptive, real-time adjustments based on tactile feedback, though it is incremental in integrating existing methods into a new design.
The paper tackles the problem of rigid-soft interactive grasping by designing a soft tactile finger with optical fibers and using machine learning for real-time prediction of force, torque, and contact, achieving enhanced grasping robustness as validated through experiments.
This paper presents a novel design of a soft tactile finger with omni-directional adaptation using multi-channel optical fibers for rigid-soft interactive grasping. Machine learning methods are used to train a model for real-time prediction of force, torque, and contact using the tactile data collected. We further integrated such fingers in a reconfigurable gripper design with three fingers so that the finger arrangement can be actively adjusted in real-time based on the tactile data collected during grasping, achieving the process of rigid-soft interactive grasping. Detailed sensor calibration and experimental results are also included to further validate the proposed design for enhanced grasping robustness.