迈向机器人灵巧手智能：一篇综述
Towards Robotic Dexterous Hand Intelligence: A Survey
AI总结：
本文综述了灵巧手研究，从硬件、控制方法、数据集和评估等方面分析，探讨了当前研究的局限性和未来发展方向。
摘要：
机器人灵巧手在接触丰富操作中至关重要，其快速进步由硬件、传感、控制、模拟和数据生成的进展推动。然而，现有研究通常基于不同的假设，如手部结构、传感配置、任务设置、训练数据和评估协议，使系统比较困难并模糊了该领域的发 展轨迹。本文从四个方面全面回顾了灵巧手研究。首先，我们进行了硬件层面的分析，涵盖驱动、传动、感知和代表性手部设计，突出力能力、顺应性、带宽、集成和系统复杂性之间的关键权衡。此外，我们从方法论角度回顾了灵巧操作的控制和学习方法，按主要范式分类代表性工作，并按时间顺序追踪其演变。此外，我们整合了数据集、模态设计和评估实践，使方法论进步能够与训练、基准测试和评估的方式一起解释。最后，我们讨论了当前灵巧手研究的主要局限性，并总结了相应的未来方向。通过连接硬件分析、方法论发展、数据资源和评估，本文旨在提供对灵巧手研究的结构化理解，并明确未来研究最重要的开放挑战。
英文摘要：
Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and data generation. However, existing studies are often developed under different assumptions regarding hand embodiments, sensory configurations, task settings, training data, and evaluation protocols, making systematic comparison difficult and obscuring the developmental trajectory of the field. This survey provides a holistic review of dexterous hand research from four complementary aspects. First, we present a hardware-level analysis covering actuation, transmission, perception, and representative hand designs, highlighting the key trade-offs in force capability, compliance, bandwidth, integration, and system complexity. Furthermore, we review control and learning methods for dexterous manipulation from a methodological perspective, grouping representative works by major paradigms and tracing their evolution in chronological order. In addition, we consolidate datasets, modality design, and evaluation practices, which enables methodological progress to be interpreted together with the ways in which it is trained, benchmarked, and assessed. Finally, we discuss the major limitations of current dexterous hand research and summarize the corresponding future directions. By connecting hardware analysis, methodological development, data resources, and evaluation, this survey aims to provide a structured understanding of dexterous hand research and to clarify the most important open challenges for future study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
