Human dexterity is an invaluable capability for precise manipulation of objects in complex tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects is critical for their use in the ever changing human environment, and for their ability to replace manpower. In recent decades, significant effort has been put in order to enable in-hand manipulation capabilities to robotic systems. Initial robotic manipulators followed carefully programmed paths, while later attempts provided a solution based on analytical modeling of motion and contact. However, these have failed to provide practical solutions due to inability to cope with complex environments and uncertainties. Therefore, the effort has shifted to learning-based approaches where data is collected from the real world or through a simulation, during repeated attempts to complete various tasks. The vast majority of learning approaches focused on learning data-based models that describe the system to some extent or Reinforcement Learning (RL). RL, in particular, has seen growing interest due to the remarkable ability to generate solutions to problems with minimal human guidance. In this survey paper, we track the developments of learning approaches for in-hand manipulations and, explore the challenges and opportunities. This survey is designed both as an introduction for novices in the field with a glossary of terms as well as a guide of novel advances for advanced practitioners.

翻译：人类灵巧性是完成复杂任务中物体精准操控的宝贵能力。机器人同样具备抓取与手内操作物体的能力对其适应不断变化的人类环境及替代人力至关重要。近几十年来，研究者们为赋予机器人系统手内操作能力投入了大量努力。早期机械臂遵循精心编程的轨迹，后续尝试则基于运动与接触的解析建模提供解决方案。然而，由于无法应对复杂环境与不确定性，这些方法未能提供实用方案。因此，研究方向转向基于学习的方法——通过反复尝试完成各类任务，从现实世界或仿真中采集数据。绝大多数学习方法聚焦于学习描述系统状态的数据驱动模型，或采用强化学习（RL）。其中，强化学习因能以最少人工指导生成问题解决方案的卓越能力而受到日益关注。本综述梳理了手内操作学习方法的发展历程，探讨其挑战与机遇。本文既可作为领域新手的入门指南（附术语表），亦能为资深研究者提供前沿进展的参考。