AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions
Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimeti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/7/460 |
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| _version_ | 1849246609925210112 |
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| author | Hoejin Jung Soyoon Park Sunghoon Joe Sangyoon Woo Wonchil Choi Wongyu Bae |
| author_facet | Hoejin Jung Soyoon Park Sunghoon Joe Sangyoon Woo Wonchil Choi Wongyu Bae |
| author_sort | Hoejin Jung |
| collection | DOAJ |
| description | Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems. |
| format | Article |
| id | doaj-art-1e6dbd535bcf4979a3c85ce8692fbf84 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-1e6dbd535bcf4979a3c85ce8692fbf842025-08-20T03:58:26ZengMDPI AGBiomimetics2313-76732025-07-0110746010.3390/biomimetics10070460AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future DirectionsHoejin Jung0Soyoon Park1Sunghoon Joe2Sangyoon Woo3Wonchil Choi4Wongyu Bae5Department of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaDepartment of Electrical Engineering, Soongsil University, Seoul 06978, Republic of KoreaBiomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems.https://www.mdpi.com/2313-7673/10/7/460AI-driven controlAI-algorithmbiomimitic robot |
| spellingShingle | Hoejin Jung Soyoon Park Sunghoon Joe Sangyoon Woo Wonchil Choi Wongyu Bae AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions Biomimetics AI-driven control AI-algorithm biomimitic robot |
| title | AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions |
| title_full | AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions |
| title_fullStr | AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions |
| title_full_unstemmed | AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions |
| title_short | AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions |
| title_sort | ai driven control strategies for biomimetic robotics trends challenges and future directions |
| topic | AI-driven control AI-algorithm biomimitic robot |
| url | https://www.mdpi.com/2313-7673/10/7/460 |
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