综述灵巧机器人操作的演进与挑战，聚焦数据收集与学习框架。
The Developments and Challenges towards Dexterous and Embodied Robotic Manipulation: A Survey
- 从机械编程到具身智能，手型从简单夹爪向多指灵巧手演进
- 仿真、人类示范与遥操作推动灵巧操作数据积累，模仿与强化学习提升技能学习效率
- 揭示三大核心挑战：数据质量、泛化能力与真实场景适应性，适合研究者参考
实现类人灵巧机器人操作仍是机器人领域的核心目标与关键挑战。人工智能的发展推动了机器人操作技术的快速进步。本文综述了机器人操作从机械编程到具身智能的演变过程，以及从简单夹爪到多指灵巧手的形态演进，概述了其关键特征与主要挑战。聚焦当前具身灵巧操作阶段，重点分析了两个关键方向的进展：灵巧操作数据采集（通过仿真、人类示范和遥操作）与技能学习框架（模仿学习与强化学习）。基于现有数据采集范式与学习框架的梳理，总结并讨论了制约灵巧机器人操作发展的三大核心挑战。
原文摘要 · Abstract (English)
Achieving human-like dexterous robotic manipulation remains a central goal and a pivotal challenge in robotics. The development of Artificial Intelligence (AI) has allowed rapid progress in robotic manipulation. This survey summarizes the evolution of robotic manipulation from mechanical programming to embodied intelligence, alongside the transition from simple grippers to multi-fingered dexterous hands, outlining key characteristics and main challenges. Focusing on the current stage of embodied dexterous manipulation, we highlight recent advances in two critical areas: dexterous manipulation data collection (via simulation, human demonstrations, and teleoperation) and skill-learning frameworks (imitation and reinforcement learning). Then, based on the overview of the existing data collection paradigm and learning framework, three key challenges restricting the development of dexterous robotic manipulation are summarized and discussed.
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