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Improving performance of robots using human-inspired approaches: a survey

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Abstract

Realizing high performance of ordinary robots is one of the core problems in robotic research. Improving the performance of ordinary robots usually relies on the collaborative development of multiple research fields, resulting in high costs and difficulty to complete some high-precision tasks. As a comparison, humans can realize extraordinary overall performance under the condition of limited computational-energy consumption and low absolute precision in sensing and controlling each body unit. Therefore, developing human-inspired robotic systems and algorithms is a promising avenue to improve the performance of robotic systems. In this review, the cutting-edge research work on human-inspired intelligent robots in decision-making, cognition, motion control, and system design is summarized from behavior- and neural-inspired aspects. This review aims to provide a significant insight into human-inspired intelligent robots, which may be beneficial for promoting the integration of neuroscience, machinery, and control, so as to develop a new generation of robotic systems.

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Acknowledgements

This work was supported by Major Project of Science and Technology Innovation 2030—Brain Science and Brain-Inspired Intelligence (Grant No. 2021ZD0200408), National Natural Science Foundation of China (Grant Nos. 91948303, 62203443), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB32050100), Science Foundation for Youth of the State Key Laboratory of Management and Control for Complex System (Grant No. 2022QN09). The authors would like to thank Jie GAO, Zhengyu LIU, Chaojing YAO, Yaxiong WU, and Peijie YIN for their help in suggestions and writing.

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  1. State Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Hong Qiao, Shanlin Zhong, Ziyu Chen & Hongze Wang

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China

    Hong Qiao, Shanlin Zhong, Ziyu Chen & Hongze Wang

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China

    Hong Qiao

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Qiao, H., Zhong, S., Chen, Z. et al. Improving performance of robots using human-inspired approaches: a survey. Sci. China Inf. Sci. 65, 221201 (2022). https://doi.org/10.1007/s11432-022-3606-1

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