arXiv 2312.050232023-12-08cs.ROcs.AIcs.LG

基于强化学习并利用域随机化的拟人机器人抓取仿生反射控制

Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization

Indian Institute of Technology Guwahati（印度古瓦哈蒂印度理工学院）；Biomimetic Robotics and Artificial Intelligence Laboratory (BRAIL)（仿生机器人与人工智能实验室（BRAIL））

Hirakjyoti Basumatary, Daksh Adhar, Atharva Shrawge, Prathamesh Kanbaskar, Shyamanta M. Hazarika

备注：: This work has been submitted to the IEEE for possible publication

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AI总结：

针对机器人手抓取易滑移、变形且传统仿生反射控制依赖人工阈值与标注的问题，提出基于强化学习并结合域随机化的自主控制流程，在 PyBullet 中验证其 Sim2Real 迁移潜力。

摘要：

在机器人抓取中实现人类水平的灵巧性仍是一项具有挑战性的工作。机器人手在物体操作过程中经常遇到滑移和变形问题，而人类由于拥有感觉感受器、经验学习和运动记忆，很少遇到这些问题。在机器人手中模拟人类抓握反射被称为“仿生反射”。以往仿生反射控制领域的尝试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工干预。在本研究中，我们提出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对人工干预的需求。我们提出的仿生反射控制器已在一只拟人化手上完成设计和测试，在 PyBullet 物理模拟器中操作可变形物体，并引入域随机化（DR）以增强 Sim2Real 可迁移性。研究结果强调了 RL 作为推进拟人机器人手仿生反射控制的有力工具的前景。我们预计，这种自主的、基于 RL 的仿生反射控制器将推动可靠且高效的机器人手与假肢手的发展，彻底改变人机交互和辅助技术。

英文摘要：

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.

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