Abstract Designing a robot system with reasoning and learning ability has gradually become a research focus in robotics research field. Recently, Skill Transfer Learning (STL), i.e., the ability of transferring human skills to robots, has become a research thrust for autonomous robots and human–robot cooperation. It provides the following benefits: (i) the skill transfer learning system with independent decision-making and learning ability enables the robot to learn and acquire manipulation skills in a complex and dynamic environment, which can overcome the shortages of conventional methods such as traditional programming, and greatly improve the adaptability of the robot to complex environments and (ii) human physiological signals allow us to extract motion control characteristics from physiological levels which create a rich sensory signal. In this survey, we provide an overview of the most important applications of STL by analyzing and categorizing existing works in autonomous robots and human–robot cooperation area. We close this survey by discussing remaining open challenges and promising research topics in future.

中文翻译：

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自主机器人和人机合作的技能迁移学习：一项调查

摘要 设计具有推理和学习能力的机器人系统逐渐成为机器人研究领域的研究热点。最近，技能转移学习（STL），即将人类技能转移到机器人的能力，已成为自主机器人和人机合作的研究重点。它提供了以下好处：（i）具有独立决策和学习能力的技能迁移学习系统，使机器人能够在复杂动态的环境中学习和掌握操作技能，克服了传统编程等常规方法的不足，并大大提高机器人对复杂环境的适应性和（ii）人类生理信号使我们能够从生理层面提取运动控制特征，从而产生丰富的感官信号。在本次调查中，我们通过分析和分类自主机器人和人机合作领域的现有作品，概述了 STL 最重要的应用。我们通过讨论未来存在的开放性挑战和有前途的研究课题来结束本次调查。