Towards Robotic Dexterous Hand Intelligence: A Survey

2026-05-13

AI Summary

Current research on dexterous hands lacks standardized benchmarks across hardware design, perception modalities, task formulations, datasets, and evaluation protocols, hindering systematic comparison and progress. This work presents the first integrative framework that cohesively unifies hardware architecture, control and learning methodologies, datasets, and evaluation criteria into a four-dimensional analytical structure to systematically trace the field’s technical evolution. By encompassing actuation and transmission mechanisms, multimodal sensing, learning-based control, simulation-to-reality data generation, and standardized assessment metrics, the study elucidates the interdependencies among these dimensions, identifies prevailing limitations, and articulates key open challenges. The contributions include a structured taxonomy and an evolutionary roadmap for dexterous hand research, offering a clear technical trajectory and guiding future investigations toward critical unresolved problems.

Abstract

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.

Problem

• dexterous hand
• systematic comparison
• evaluation protocols
• research assumptions
• developmental trajectory

Innovation

• dexterous hand
• robotic manipulation
• hardware-software co-design
• learning-based control
• benchmarking