机器人操作的鲁棒性：基础与前沿
Robustness of Robotic Manipulation: Foundations and Frontiers
AI总结：
本文系统研究机器人操作鲁棒性，提出正式定义、概率与控制理论框架，并综述感知、规划、控制、策略学习和硬件中的鲁棒性机制及评估方法，展望实现人类级鲁棒性的未来方向。
摘要：
人类和动物在物理操作中表现出显著的鲁棒性，但机器人仍远远落后。由于缺乏统一和系统的理解，向人类级操作鲁棒性的进展受到阻碍：不同子领域以不同方式定义鲁棒性，往往使概念模糊，限制深入分析以及跨研究领域的交流。本文对操作鲁棒性进行了系统研究。我们首先给出正式定义，将鲁棒性表征为操作系统在存在不确定性和变化时实现其目标的程度。基于这一定义，我们从概率和控制理论的角度介绍了操作鲁棒性的一般公式。然后，我们综合了感知、规划、控制、策略学习和硬件中操作鲁棒性的指导原则和具体机制，通过代表性工作（包括基础研究和近期研究）说明每种机制。此外，我们重新审视了用于量化操作鲁棒性的现有指标和评估方法。最后，我们提炼出设计鲁棒操作系统的更广泛经验，并讨论了实现机器人操作中人类级鲁棒性的开放问题和未来方向。
英文摘要：
Humans and animals exhibit remarkable robustness in physical manipulation, yet robots remain far behind. Progress toward human-level manipulation robustness is hindered by the absence of a unified and systematic understanding: different subfields frame robustness in distinct ways, often leaving the concept ambiguous and limiting deeper analysis as well as communication across research areas. This paper presents a systematic study of manipulation robustness. We begin with a formal definition, characterizing robustness as the degree to which a manipulation system can achieve its goal in the presence of uncertainty and variation. Building on this definition, we introduce general formulations of manipulation robustness from probabilistic and control-theoretic perspectives. We then synthesize the guiding principles and concrete mechanisms of manipulation robustness across perception, planning, control, policy learning, and hardware, illustrating each mechanism through representative works, including foundational and recent studies. In addition, we revisit existing metrics and evaluation methods for quantifying manipulation robustness. Finally, we distill broader lessons for designing robust manipulation systems and discuss open problems and future directions toward achieving human-level robustness in robotic manipulation.
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。
