  Achieving human-level dexterity in robotic grasping remains a challenging
endeavor. Robotic hands frequently encounter slippage and deformation during
object manipulation, issues rarely encountered by humans due to their sensory
receptors, experiential learning, and motor memory. The emulation of the human
grasping reflex within robotic hands is referred to as the ``bionic reflex".
Past endeavors in the realm of bionic reflex control predominantly relied on
model-based and supervised learning approaches, necessitating human
intervention during thresholding and labeling tasks. In this study, we
introduce an innovative bionic reflex control pipeline, leveraging
reinforcement learning (RL); thereby eliminating the need for human
intervention during control design. Our proposed bionic reflex controller has
been designed and tested on an anthropomorphic hand, manipulating deformable
objects in the PyBullet physics simulator, incorporating domain randomization
(DR) for enhanced Sim2Real transferability. Our findings underscore the promise
of RL as a potent tool for advancing bionic reflex control within
anthropomorphic robotic hands. We anticipate that this autonomous, RL-based
bionic reflex controller will catalyze the development of dependable and highly
efficient robotic and prosthetic hands, revolutionizing human-robot interaction
and assistive technologies.
