Review on Dexterous Hand Technology Empowered by Computer Science: Multidimensional Integration and Innovative Progress

Di Li

doi:10.62051/abgdx687

Authors

Di Li

DOI:

https://doi.org/10.62051/abgdx687

Keywords:

Dexterous Hand; Computer Science; context-aware learning simulation-based training; Multidimensional Integration.

Abstract

Dexterous hand technologies have emerged as a critical frontier in robotics, driven by growing demands for human-like manipulation in complex and dynamic environments. This review explores how advancements in computer science—particularly in intelligent control, sensor fusion, and machine learning—have transformed the design and deployment of dexterous robotic hands. We analyze three major integration pathways: data-driven control algorithms that improve real-time adaptability, application-specific solutions in domains such as healthcare and manufacturing, and multimodal perception frameworks that enhance manipulation precision through the fusion of tactile, visual, and proprioceptive data. The review also identifies key challenges, including perception latency, data scarcity, limited generalization, and deployment barriers. Finally, we propose future research directions involving context-aware learning, simulation-based training, lightweight architectures, and interdisciplinary collaboration. The findings underline the importance of computational intelligence in enabling robotic hands to achieve robust, safe, and flexible performance in real-world settings.

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References

[1] Sarmah, S.B.; Kalita, P.; Garg, A.; Niu, X.-D.; Zhang, X.-W.; Peng, X.; Bhattacharjee, D. A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles. J. Electrochem. Energy Convers. Storage 2019, 16, 040801.

[2] Hasib, S.A.; Islam, S.; Chakrabortty, R.K.; Ryan, M.J.; Saha, D.K.; Ahamed, H.; Moyeen, S.I.; Das, S.K.; Ali, F.; Islam, R.; et al. A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management. IEEE Access 2021, 9, 86166–86193.

[3] Elmahallawy, M.; Elfouly, T.; Alouani, A.; Massoud, A.M. A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction. IEEE Access 2022, 10, 119040–119070.

[4] Su, N.K.H.; Juwono, F.H.; Wong, W.K.; Chew, I.M. Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries. In Proceedings of the 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), Miri Sarawak, Malaysia, 26–28 October 2022; pp. 286–292.

[5] Wang, Y.; Zuo, X. State of Charge Estimation Methods and Application Scenarios of Lithium-Ion Batteries. Power Syst. Autom. 2022, 32-1180/TP, 1–15.

[6] He, L.; Hu, M.; Shi, Q.; Ye, W. A staged state of charge estimation algorithm for lithium-ion batteries. Power Electron. Technol. 2020, 54, 8–11.

[7] Wu, L.; Pang, H.; Jin, J.; Geng, Y.; Liu, K. A Review of State of Charge Estimation Methods for Lithium Ion Batteries Based on Electrochemical Models. J. Electr. Technol. 2022, 37, 1703–1725.

[8] Wu, C.; Hu, W.; Meng, J.; Liu, Z.; Cheng, D. State of Charge Estimation of Lithium Ion Battery Based on Maximum Correlation Entropy Extended Kalman Filter Algorithm. J. Electr. Technol. 2021, 36, 5165–5175.

[9] Li, C.; Xiao, F.; Fan, Y.; Yang, G.; Tang, X. State of Charge Estimation of Lithium Ion Battery Based on Gated Cyclic Unit Neural Network and Huber-M Estimation Robust Kalman Filter Fusion Method. J. Electr. Technol. 2020, 35, 2051–2062.

[10] Wang, X.; Zheng, C.; Zhang, S.; Dai, M.; Xiao, W.; Chen, X. Summary of fusing algorithm research in estimation for state of charge of battery. Sichuan Electr. Power Technol. 2021, 44, 43–46.

[11] Li, Z.; Lu, L.; Ouyang, M. Comparison of Methods to Improve the Accuracy of Battery SOC Estimation by Ampere Hour Integration. J. Tsinghua Univ. 2010, 50, 1293–1296.

[12] Luo, Y.; Qi, P.; Huang, H.; Wang, J.; Wang, Y.; Li, P. Research on Estimation Method of Ampere Hour Integration SOC Based on Capacity Correction. Automot. Eng. 2020, 42, 681–687.

[13] Edge, J.; Arce, A.; Barrios, R.; Alavi, S.M. Practical Implications of Delayed State Estimation in Smart Battery Management. Energies 2021, 14, 3124.

[14] Xie, Z.; Liu, Y.; Wu, J. Improved Particle Filtering Algorithm for Nonlinear Time-Varying Systems with Time Delay. Proc. CSEE 2018, 38, 1332–1340.

[15] Zhang, M.; Wang, Y.; Zhang, Y. Adaptive Filter Design for Lithium-Ion Battery Online Estimation under Variable Environments. J. Power Supply 2018, 16, 58–63.

[16] Huang, Y.; Xiong, R.; He, H. Adaptive Noise Modeling and Filtering for Online State-of-Charge Estimation of Lithium-Ion Batteries. J. Power Electron. 2019, 19, 580–589.

[17] Ming, J.; Shi, S.; Du, Y.; Luo, T. Deep Recurrent Neural Network for Generalized State Estimation in EV Battery Packs. J. Electr. Eng. 2021, 39, 421–430.

[18] Zheng, Y.; Zhou, Y.; Wang, C.; Peng, H.; Zhou, H. Model Compression for Portable Battery Management Systems. J. Tsinghua Univ. (Sci. Technol.) 2018, 58, 913–918.

[19] Dang, C.; Hu, X.; Wang, Z.; Wang, Y. Online State of Charge Estimation for Power Batteries Using an Adaptive Dual-Model Kalman Filter. Trans. China Electrotech. Soc. 2017, 32, 164–170.

[20] Schmidt, A.P.; Silva, F.A.; Andrade, C.H. Embedded Estimation and Control Algorithms for Energy-Constrained Devices. Embedded Syst. Lett. 2010, 2, 77–81.

[21] “Available online: http://kns.cnki.net/kcms/detail/32.1180.TP.20220331.1418.002.html” (accessed on 14 April 2022). Reference [19] in original.

[22] He, H.; Xiong, R.; Huang, Y. Multi-Scale Degradation Modeling for Lithium-Ion Battery Health Estimation in Operational Environments. J. Energy Storage 2011, 3, 52–60.

[23] Jiao, Y.; Zhang, L.; Wang, H.; Huang, Z.; Liu, Z. Online Parameter Identification and Transferable Estimation Framework for Battery Health Monitoring. Energy Rep. 2020, 6, 125–134.

[24] Moura, L.M.; Afonso, J.L.; Ferreira, H.C.; Silva, F.A. Multi-Source Data-Driven Health Forecasting Using Layered Modeling Approaches. Energies 2017, 10, 1033.

[25] Zhu, J.; Zhang, Z.; Liang, B.; Xia, X. Simulation-Aided Online Training for Battery Prognostics under Limited Data Conditions. Appl. Sci. 2017, 7, 1130.

[26] Su, N.K.H.; Juwono, F.H.; Wong, W.K.; Chew, I.M. Review on Machine Learning Methods for Remaining Useful Lifetime Prediction of Lithium-ion Batteries. In Proc. GECOST 2022, Miri, Malaysia, Oct. 2022; pp. 286–292.

[27] The key password for the "dexterous hand" of humanoid robots - tendon rope material [J]. Synthetic Fiber, 2025, 54 (03): 45

[28] Cong Ming, Wu Minjie, Du Yu, Li Yongyao Underactuated dexterous hand grasping method based on grasping pattern recognition [J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2023, 51 (06): 29-35

[29] Li Kege, Wang Rugui Performance Analysis and Simulation of Agile Hand Movement [J]. Equipment Manufacturing Technology, 2022, (10):1-4+45.

[30] She Qijin Real time planning of dexterous hand grasping based on dynamic interactive geometric expression [D]. Mentor: Xu Kai National University of Defense Technology, 2022

[31] Qi Chunxiao Research on Kinematic Simulation of Agile Hand Based on Swarm Intelligence Optimization Algorithm [D]. Mentor: Du Qiaoling Jilin University, 2022

[32] Shen Huimin, Ge Ruikang, Gu Xiaowei, Cai Xiaotong, Gan Yi, Yang Gong A method for inverting full posture information of dexterous finger units based on magnetic inertial sensing fusion [J]. Journal of Mechanical Engineering, 2022, 58 (18): 133-140

[33] Lu Baisheng Research on PDMS based flexible strain sensor for soft dexterous hand ontology perception [D]. Mentor: Liu Caixia Hefei University of Technology, 2022

[34] Fang Yuefa, Huang Yijun Design and Performance Research of Robot Agile Hand Based on Humanoid Parallel Finger Mechanism [J]. Journal of Beijing Jiaotong University, 2021, 45 (04): 146-156

[35] Yi Xinxin Research on Soft Control Technology of Agile Hand Based on Fingertip Force Touch Integrated Sensor [D]. Mentor: Liu Hong Harbin Institute of Technology, 2021

[36] Hou Haonan Design and Research of a Agile Hand with High Speed and High Grip [D]. Mentor: Ye Bosheng Huazhong University of Science and Technology, 2021

[37] Zeng Zhi, Li Kang, Yu Huijun, Fan Na, Wei Dunwen University of Electronic Science and Technology of China, development of a multi degree of freedom dexterous hand based on shape memory materials [Z]. Project approval number: 2018GZ0284. Appraisal unit: Sichuan Provincial Department of Science and Technology Appraisal date: June 11, 2020

[38] Ma Boliang Research on tactile based dexterous hand grasping control technology [D]. Mentor: Xiong Pengwen; Zheng Shujiang Nanchang University, 2020

[39] Monday Dan Research on Visual based Agile Hand Human Machine Collaborative Operation [D]. Mentor: Lin Xiangbo Dalian University of Technology, 2019

[40] Xu Peipei, Liu Xu, Zhai Jiaxing, Qiu Zecheng, Wang Guoqing Multi joint gripping dexterous hand design [J]. Construction Machinery, 2017, (01): 63-66+69

[41] Shi Hongda, Li Xiufei, Zhang Lizhong Virtual Reality Agile Hand Attitude and Force Control Algorithm and Simulation [J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2016, 39 (05): 70-75+84

[42] Chen Lei Agile Hand Synchronous Coordination Control Based on Iterative Learning Algorithm [D]. Mentor: Guan Zhihong Huazhong University of Science and Technology, 2014

[43] Yu Shiguang Design and Implementation of Control System for Master Slave Agile Hand [D]. Mentor: Yang Wenzhen Zhejiang University of Technology, 2012

[44] Martin, J., & Grossard, M. (2014). Design of a fully modular and backdrivable dexterous hand. The International Journal of Robotics Research, 33(5), 783-798.

[45] Andrychowicz, O. M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., ... & Zaremba, W. (2020). Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1), 3-20.

[46] Shang, W., Song, F., Zhao, Z., Gao, H., Cong, S., & Li, Z. (2020). Deep learning method for gras** novel objects using dexterous hands. IEEE Transactions on Cybernetics, 52(5), 2750-2762.

[47] Yousef, H., Boukallel, M., & Althoefer, K. (2011). Tactile sensing for dexterous in-hand manipulation in robotics—A review. Sensors and Actuators A: physical, 167(2), 171-187.