Li Yinlin, Wang Peng, Li Rui, Tao Mo, Liu Zhiyong, Qiao Hong
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Front Neurorobot. 2022 Apr 27;16:843267. doi: 10.3389/fnbot.2022.843267. eCollection 2022.
Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.
多指机器人手（通常称为灵巧手）旨在为机器人实现人类水平或类似人类的操作，或作为残疾人的假肢。该研究可追溯到30年前，然而，由于其配置的高维度、频繁切换的交互模式以及各种任务泛化要求，有效设计和控制它们仍然面临巨大挑战。本文旨在从三个方面简要概述多指机器人操作：a）生物学成果，b）结构演变，c）学习方法，并讨论潜在的未来方向。首先，我们研究以手为中心的视觉传感、触觉传感和运动控制的结构和原理以及相关的行为结果。然后，我们从任务场景、驱动机制和手内传感器等方面回顾几种典型的多指灵巧手。第三，我们报告各种基于学习的多指操作方法的最新进展，包括但不限于强化学习、模仿学习和其他子类方法。本文最后讨论了开放问题以及我们对未来方向的思考。
