系统梳理模仿学习在机器人操作中的演进与挑战。
Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
- 按技术路径分类分析20余篇关键论文，构建方法体系。
- 总结主流输入格式与先验知识设计，揭示性能影响因素。
- 适合想快速掌握该领域脉络的研究者与工程师。
机器人操作（RM）是推动自主机器人发展的核心，使其能在真实环境中与物体交互并完成操作任务。本文聚焦于基于模仿学习的RM方法，该技术通过模仿人类示范来学习复杂操作技能。我们根据学术影响力与研究质量筛选出该领域最具影响力的论文，对每篇进行结构化综述，涵盖研究目标、技术实现、层级分类、输入形式、关键先验、优缺点及引用数据。同时，追踪模仿学习在机器人策略（RMP）中的发展历程，呈现关键技术进展的时间线。在可获得的情况下，报告基准测试结果并进行定量对比。通过整合这些洞察，本综述为研究人员和从业者提供全面资源，揭示当前技术水平及未来面临的挑战。
原文摘要 · Abstract (English)
Robotic Manipulation (RM) is central to the advancement of autonomous robots, enabling them to interact with and manipulate objects in real-world environments. This survey focuses on RM methodologies that leverage imitation learning, a powerful technique that allows robots to learn complex manipulation skills by mimicking human demonstrations. We identify and analyze the most influential studies in this domain, selected based on community impact and intrinsic quality. For each paper, we provide a structured summary, covering the research purpose, technical implementation, hierarchical classification, input formats, key priors, strengths and limitations, and citation metrics. Additionally, we trace the chronological development of imitation learning techniques within RM policy (RMP), offering a timeline of key technological advancements. Where available, we report benchmark results and perform quantitative evaluations to compare existing methods. By synthesizing these insights, this review provides a comprehensive resource for researchers and practitioners, highlighting both the state of the art and the challenges that lie ahead in the field of robotic manipulation through imitation learning.
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