Key Takeaways & Executive Findings
- •• Bibliometric analysis reveals the evolution and current hotspots in dexterous hand grasp research, highlighting a shift toward intelligent and multimodal approaches. • Precision control via multimodal fusion, autonomous task understanding, and in-hand dexterous manipulation are identified as the top three future research directions. • The review provides a comprehensive overview of publication trends, key contributors, and collaborative networks, offering a roadmap for researchers. • The findings underscore the importance of integrating advanced sensors and AI to achieve human-like dexterity in robotic hands.
Abstract
Recent years have witnessed unprecedented development in humanoid robotics, with dexterous hand grasping emerging as a focal research area across industrial and academic sectors. To track the state-of-the-art dexterous hand grasp, a review of dexterous hand grasp based on bibliometric analysis is executed. The related studies on dexterous hand grasp are collected from the Web of Science for analysis, where the publication details and cooperation situations from the perspectives of country, institute, etc. are discussed. The keywords cluster is adopted to find the hot research topic of dexterous hand grasp. The development trend of dexterous hand grasp is explored based on the top 25 keywords with the strongest citation bursts. The review findings indicate that precision control via multimodal fusion, autonomous task understanding and intelligent decision, and in-hand dexterous manipulation are top three hotspots in future.
1. Introduction
A humanoid robot that is inspired by the shape and action of humans has become the most popular topic in recent years, and one of the core goals is imitating the object grasp with various shapes, sizes, and materials like human hands [1]. The development of dexterous hands can be traced back to the 1960s, with early representative designs including the Okada Hand and the Stanford/JPL Hand [2]. These designs featured modular structures with multiple degrees of freedom, mimicking the human hand’s grasping and manipulation capabilities. Subsequently, the DLR/HIT Hand [3] emerged as another foundational design, characterized by its multi-joint, multi-DOF structure, integrating various sensors to achieve flexible multi-finger control. These early designs not only laid the hardware foundation for dexterous hand development but also provided experimental platforms for subsequent control strategies.
In terms of control theory, the classical impedance control method was proposed by Hogan in 1985 [4], providing a theoretical basis for dexterous hands to achieve compliant manipulation in unknown environments. Force/position hybrid control was later developed to achieve independent control of force and position in different directions [5], enabling dexterous hands to balance stability and precision during fine manipulation tasks. These classical control methods not only established the theoretical foundation for dexterous hand grasping and manipulation but have also been widely applied in subsequent intelligent control methods, such as reinforcement learning and imitation learning.
However, with the advancements in sensor technologies, control theories, and artificial intelligence, dexterous hands have evolved to encompass multifunctional capabilities and intelligent operations [6, 7]. The degrees of freedom (DOF) of dexterous hands have increased from a mere 3–4 DOF to over 20 DOF, enabling robots to grasp non-standardized structured objects and perform complex tasks. Currently, robots can simulate the fine motions of the human hand and even achieve functions that surpass human capabilities in certain areas, such as operating in confined spaces or performing grasping tasks in high-temperature, toxic, and extreme environments. With the help of advanced algorithms, robots can dynamically adjust their grasping strategies in real-time by perceiving object features and optimizing their adjustments in uncertain environments.
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Zhe Xu, Sihan Huang, Liya Yao, Jiahao Zhu, Guoxin Wang, Yan Yan (2025). Grasp Control of Dexterous Hands Based on Bibliometric Analysis: A Survey. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01346-z
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Frequently Asked Questions
What is the main focus of this review paper?
This review paper focuses on the grasp control of dexterous hands, using bibliometric analysis to survey the state-of-the-art research, identify hotspots, and predict future trends in the field.
What are the top three future research hotspots identified in the paper?
The top three future hotspots are precision control via multimodal fusion, autonomous task understanding and intelligent decision, and in-hand dexterous manipulation.
How was the bibliometric analysis conducted?
The analysis was based on publications from the Web of Science, examining publication details, cooperation patterns among countries and institutes, keyword clusters, and citation bursts to identify trends.
What is the significance of this review for researchers in robotics?
It provides a comprehensive overview of the field, highlighting key research directions and collaborative networks, which can guide researchers in selecting impactful topics and potential collaborations.
What are the key technologies discussed for dexterous hand grasp control?
The paper discusses classical control methods like impedance and force/position hybrid control, as well as modern intelligent approaches such as reinforcement learning and imitation learning, along with sensor integration for perception.
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