Robustness of Robotic Manipulation: Foundations and Frontiers

2026-06-30

AI Summary

Current research in robotic manipulation lacks a unified definition and systematic understanding of “manipulation robustness,” leading to fragmented progress. This work addresses this gap by formally defining manipulation robustness as the system’s ability to achieve task objectives under uncertainty and perturbations. Building upon probability theory and control theory, we develop a general analytical framework that integrates perception, planning, control, policy learning, and hardware co-design. The study systematically distills cross-modal robustness design principles, redefines the evaluation metric体系, and identifies core pathways and key challenges toward human-level robustness. In doing so, it establishes a comprehensive theoretical foundation encompassing definition, mechanisms, evaluation, and design—providing essential support for advancing highly robust robotic manipulation.

Abstract

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.

Problem

• robustness
• robotic manipulation
• uncertainty
• systematic understanding
• human-level performance

Innovation

• manipulation robustness
• formal definition
• probabilistic formulation
• control-theoretic framework
• systematic synthesis