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.

翻译：实现机器人抓取的人类级灵巧性仍是一项具有挑战性的任务。机器手在操作物体时常遇到滑移和形变问题，而人类由于拥有感觉受体、经验学习及运动记忆，极少出现此类情况。在机器手中模拟人类抓取反射的行为被称为“仿生反射”。过往的仿生反射控制研究主要依赖基于模型和监督学习的方法，在阈值设定和标注任务中需要人为干预。本研究中，我们引入了一种创新的仿生反射控制流程，利用强化学习（RL），从而消除了控制设计过程中的人为干预需求。我们所提出的仿生反射控制器在拟人手上进行了设计与测试，在PyBullet物理模拟器中操作可变形物体，并采用域随机化（DR）以增强Sim2Real迁移能力。研究结果强调了RL作为增强仿人机器手仿生反射控制的强大工具的潜力。我们预期，这种基于RL的自主仿生反射控制器将推动可靠且高效的机器手与假肢手的发展，从而革新人类-机器人交互及辅助技术。