Bio-Robotics

Research article

Comparative Study of Artificial Intelligence Methods in Biohybrid Robot Control

Downloads

https://doi.org/10.54963/br.v2i1.2086
Chen, Y.-H., & Chen, Y.-J. (2026). Comparative Study of Artificial Intelligence Methods in Biohybrid Robot Control. Bio-Robotics, 2(1), 1–20. https://doi.org/10.54963/br.v2i1.2086
Volume 2 Issue 1 (2026)

Authors

  • Yung-Hsiang Chen

    Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung 912301, Taiwan
  • Yue-Jun Chen

    Department of Mechanical Engineering, National Pingtung University of Science and Technology, Pingtung 912301, Taiwan

Received: 14 December 2025; Revised: 23 January 2026; Accepted: 30 January 2026; Published: 5 February 2026

Biohybrid robots, which integrate living biological components such as muscle or neural tissues with artificial mechanical structures, represent a distinct class of systems capable of highly adaptive and biologically inspired movements. Compared to conventional robots, these platforms harness the intrinsic properties of living tissues, including self-repair, high power-to-weight ratios, and natural responsiveness to biochemical and physical stimuli. However, selecting optimal control strategies involves navigating significant trade-offs between stability, learning efficiency, and implementation complexity. Biological actuators are fundamentally constrained by nonlinear dynamics and "physiological drift" caused by metabolic fluctuations and fatigue, necessitating controllers that can adapt in real-time to maintain functionality. This paper provides a systematic comparison of AI methods applied to biohybrid control over the past five years, including deep learning, reinforcement learning, hybrid intelligent control, and data-driven adaptive models. The study reveals that hybrid intelligent control currently offers the most practical and balanced solution by embedding AI adaptability within classical stability frameworks. By partitioning the controller into model-based and learning-based components, this paradigm maintains formal safety guarantees while exploiting AI for dynamic compensation. Nevertheless, its implementation remains constrained by high architectural complexity and the difficulty of formally validating the interaction between discrete AI logic and continuous biological feedback. Finally, future research directions such as metabolic-aware control and sustainable intelligent systems are discussed to provide theoretical guidance for advancing robust AI-driven biohybrid robotics.

Keywords:

Biohybrid Robot Artificial Intelligence Reinforcement Learning Deep Learning Intelligent Control Adaptive Mechanism

References

  1. Kim, M.S.; Kim, Y.H.; Choi, Y.; et al. Biohybrid Actuators in Robotics: Recent Trends and Future Perspectives of Skeletal and Cardiac Muscle Integration. npj Robot. 2025, 3, 37.
  2. Chen, Z.; Chen, J.; Jung, S.; et al. Bioinspired and Biohybrid Soft Robots: Principles and Emerging Technologies. Matter 2025, 8, 102045.
  3. Li, T.; Takeuchi, S. Advancing Biohybrid Robotics: Innovations in Contraction Models, Control Techniques, and Applications. Bioeng. Rev. 2024, 6, 25–48.
  4. Ahmad, U. Biohybrid Microrobots: A New Frontier in Targeted Drug Delivery and Precision Therapeutics. J. Biotechnol. Bioeng. 2025, 8, 15–26.
  5. Won, P.; Ko, S.H.; Majidi, C.; et al. Biohybrid Actuators for Soft Robotics: Challenges in Scaling Up. Actuators 2020, 9, 96.
  6. Mompó Alepuz, A.; Papageorgiou, D.; Tolu, S. Brain-Inspired Biomimetic Robot Control: A Review. Front. Neurorobot. 2024, 18, 1395617.
  7. Jung, H.; Park, S.; Joe, S.; et al. AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions. Biomimetics 2025, 10, 460.
  8. Sarker, A.; Islam, T.U.; Islam, M.R. A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects. Adv. Intell. Syst. 2024, 7, 2400414.
  9. Bawa, M.; Raman, R. Taking Control: Steering the Future of Biohybrid Robots. Sci. Robot. 2024, 9, eadr9299.
  10. van Laake, L.C.; Overvelde, J.T.B. Bio-Inspired Autonomy in Soft Robots. Commun. Mater. 2024, 5, 198.
  11. Kuplinov, G.N. The Biohybrid Autonomous Robots (BAR): A Feasibility of Implementation. Front. Robot. AI 2025, 12, 1695262.
  12. Wang, X.; Wei, R.; Chen, Z.; et al. Bioinspired Intelligent Soft Robotics: From Multidisciplinary Integration to Next-Generation Intelligence. Adv. Sci. 2025, 12, e06296.
  13. Chen, Z.; Renda, F.; Le Gall, A.; et al. Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review. IEEE Trans. Robot. 2024, 40, 2241–2256.
  14. Drennan, W.C.; Aydin, O.; Emon, B.; et al. A Fast, Muscle-Actuated Biohybrid Swimming Robot. bioRxiv preprint 2024. DOI: https://doi.org/10.1101/2024.10.30.621139
  15. Pal, A.; He, T.; Wei, W. Sample-Efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization. arXiv preprint 2022, arXiv:2210.08780. DOI: https://doi.org/10.48550/arXiv.2210.08780
  16. López-González, A.; Tejada, J.; Rojas, N.; et al. Recent Advances in Neurotechnology-Based Biohybrid Robots. Soft Matter 2024, 20, 7993–8011.
  17. López-González, A.; Tejada, J.C.; López-Romero, J. Review and Proposal for a Classification System of Soft Robots Inspired by Animal Morphology. Biomimetics 2023, 8, 192.
  18. Sapai, S.; Loo, J.Y.; Ding, Z.Y.; et al. A Deep Learning Framework for Soft Robots with Synthetic Data. Soft Robot. 2023, 10, 1224–1240.
  19. De Barrie, D.; Pandya, M.; Pandya, H.; et al. A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data. Front. Robot. AI. 2021, 8, 631371.
  20. Zhang, Z.; Jin, Z.; Gu, G. Efficient Pneumatic Actuation Modeling Using Hybrid Physics-Based and Data-Driven Framework. Cell Rep. Phys. Sci. 2022, 3, 100842.
  21. Sun, Z.; Wang, S.; Zhao, Y.; et al. Discriminating Soft Actuators’ Thermal Stimuli and Mechanical Deformation by Hydrogel Sensors and Machine Learning. Adv. Intell. Syst. 2022, 4.
  22. Schäfke, H.; Habich, T.L.; Muhmann, C.; et al. Learning-Based Nonlinear Model Predictive Control of Articulated Soft Robots Using Recurrent Neural Networks. IEEE Robot. Autom. Lett. 2024, 9.
  23. Loo, J.Y.; Ding, Z.Y.; Baskaran, V.M.; et al. Robust Multimodal Indirect Sensing for Soft Robots Via Neural Network-Aided Filter-Based Estimation. Soft Robot. 2022, 9, 591–612.
  24. Ni, Y.; Wakimoto, S.; Tian, W.; et al. Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning. Sensors 2025, 25, 2221.
  25. Masoud, A.E.; Maas, J. Data-Driven Modeling and Identification of a Bistable Soft-Robot Element Based on Dielectric Elastomer. Front. Robot. AI 2025, 12, 1546945.
  26. Luu, D.K.; Nguyen, A.T.; Jiang, M.; et al. Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals. Front. Neurosci. 2021, 15, 667907.
  27. Zhang, A.; Truby, R.L.; Chin, L.; et al. Vision-Based Sensing for Electrically-Driven Soft Actuators. IEEE Robot. Autom. Lett. 2022, 7, 11509–11516.
  28. Johnson, J.J.; Yip, M. Motion Planning Using Transformers. IEEE Robot. Autom. Lett. 2023, 8, 994–1001.
  29. Sanghai, N.; Brown, N.B. Advances in Transformers for Robotic Applications: A Review. arXiv preprint 2024, arXiv:2412.10599. DOI: https://doi.org/10.48550/arXiv.2412.10599
  30. Webster-Wood, V.A.; Guix, M.; Xu, N.W.; et al. Biohybrid Robots: Recent Progress, Challenges, and Perspectives. Bioinspir. Biomim. 2023, 18, 015001.
  31. Stiti, C.; Benrabah, M.; Aouaichia, A.; et al. Lyapunov-Based Neural Network Model Predictive Control Using Metaheuristic Optimization Approach. Sci. Rep. 2024, 14, 18760.
  32. Nghiem, T.X.; Drgoňa, J.; Jones, C.; et al. Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. arXiv preprint 2023, arXiv:2306.13867. DOI: https://doi.org/10.48550/arXiv.2306.13867
  33. Elguea-Aguinaco, I.; Inziarte-Hidalgo, I.; Bøgh, S.; et al. A Review on Reinforcement Learning for Motion Planning of Robotic Manipulators. Int. J. Intell. Syst. 2024, 1636497.
  34. Schaffer, S.; Pamu, H.H.; Webster-Wood, V.A. Hitting the Gym: Reinforcement Learning Control of Exercise-Strengthened Biohybrid Robots in Simulation. arXiv preprint 2024, arXiv:2408.16069. DOI: https://doi.org/10.48550/arXiv.2408.16069
  35. Kim, Y.; Yang, Y.; Zhang, X.; et al. Remote Control of Muscle-Driven Miniature Robots with Battery-Free Wireless Optoelectronics. Sci. Robot. 2023, 8, eadd1053.
  36. Liu, Y.; Zou, Z.; Pak, O.S.; et al. Learning to Cooperate for Low-Reynolds-Number Swimming: A Model Problem for Gait Coordination. Sci. Rep. 2023, 13, 9397.
  37. Jiao, Y.; Ling, F.; Heydari, S.; et al. Deep Dive into Model-Free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice. arXiv preprint 2024, arXiv:2405.11457. DOI: https://doi.org/10.48550/arXiv.2405.11457
  38. Mozafarinia, H.; Zabetnia, A. Reinforcement Learning for Efficient Locomotion of Bio-Inspired Microswimmers in Low Reynolds Number Fluids. bioRxiv preprint 2025. DOI: https://doi.org/10.1101/2025.07.14.664791
  39. Avtges, J.; Ketchum, J.; Schlafly, M.; et al. Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot. arXiv preprint 2025, arXiv:2509.19525. DOI: https://doi.org/10.48550/arXiv.2509.19525
  40. Garg, S.; Goharimanesh, M.; Sajjadi, S.; et al. Autonomous Control of Soft Robots Using Safe Reinforcement Learning and Covariance Matrix Adaptation. Eng. Appl. Artif. Intell. 2025, 153, 110791.
  41. Wang, Y.; Wu, S.; Fu, H.; et al. Curriculum-Based Co-Design of Morphology and Control of Voxel-Based Soft Robots. In Proceedings of the International Conference on Learning Representations, Kigali, Rwanda, 1–5 May 2023.
  42. Strgar, L.; Kriegman, S. Accelerated Co-Design of Robots Through Morphological Pretraining. arXiv preprint 2025, arXiv:2502.10862. DOI: https://doi.org/10.48550/arXiv.2502.10862
  43. Tang, C.; Abbatematteo, B.; Hu, J.; et al. Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes. Annu. Rev. Control Robot. Auton. Syst. 2025, 8, 153–188.
  44. Tiboni, G.; Protopapa, A.; Tommasi, T.; et al. Domain Randomization for Robust, Affordable and Effective Closed-Loop Control of Soft Robots. arXiv preprint 2024, arXiv:2303.04136. DOI: https://doi.org/10.48550/arXiv.2303.04136
  45. Brunke, L.; Greeff, M.; Hall, A.W.; et al. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning. Annu. Rev. Control Robot. Auton. Syst. 2022, 5, 411–444.
  46. Li, C.; He, S.; Xu, Y.; et al. Model and Control of Hybrid Hard-Soft Robots Using Model Predictive Control. In Proceedings of the 41st Chinese Control Conference, Hefei, China, 25–27 July 2022.
  47. Khodaverdian, A.; Gohil, D.; Christofides, P.D. Neural Network Implementation of Model Predictive Control with Stability Guarantees. Digit. Chem. Eng. 2025, 16, 100262.
  48. Han, J.; Shan, X.; Liu, H.; et al. Fuzzy Gain Scheduling PID Control of a Hybrid Robot Based on Dynamic Characteristics. Mech. Mach. Theory 2023, 184, 105283.
  49. Khan, N.A.; Jamwal, P.K.; Hussain, F.; et al. Reinforcement Learning-Driven Path Generation for Ankle Rehabilitation Robot Using Musculoskeletal-Informed Energy Optimization. IEEE Trans. Neural Syst. Rehabil. Eng. 2025, 33, 1774–1784.
  50. Dai, Y.; Deng, Z.; Wang, X.; et al. A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control. Sensors 2023, 23, 1272.
  51. Shi, L.; Mucchiani, C.; Karydis, K. Online Modeling and Control of Soft Multi-Fingered Grippers via Koopman Operator Theory. arXiv preprint 2022, arXiv:2206.10707. DOI: https://doi.org/10.48550/arXiv.2206.10707
  52. Cangan, B.G.; Navarro, S.E.; Yang, B.; et al. Model-Based Disturbance Estimation for a Fiber-Reinforced Soft Manipulator Using Orientation Sensing. arXiv preprint 2022, arXiv:2206.11626. DOI: https://doi.org/10.48550/arXiv.2206.11626
  53. Licher, J.; Bartholdt, M.; Krauss, H.; et al. Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory. arXiv preprint 2025, arXiv:2508.12681. DOI: https://doi.org/10.48550/arXiv.2508.12681
  54. Shin, M.; Ha, T.; Lim, J.; et al. Human Motor System-Based Biohybrid Robot-on-a-Chip for Drug Evaluation of Neurodegenerative Disease. Adv. Sci. 2024, 11, e2305371.
  55. Yang, Y.; Bartolozzi, C.; Zhang, H.H.; et al. Neuromorphic Electronics for Robotic Perception, Navigation and Control: A Survey. Eng. Appl. Artif. Intell. 2023, 126, 106838.
  56. Garriga-Casanovas, A.; Shakib, F.; Ferrandy, V.; et al. Hybrid Control of Soft Robotic Manipulator. Actuators 2024, 13, 242.
  57. Wang, L.; Chen, K.; Franco, E. Robust and Adaptive Control of a Soft Continuum Manipulator for Minimally Invasive Surgery. Robotics 2024, 13, 162.
  58. Chen, Z.; Xia, Y.; Liu, J.; et al. Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots. arXiv preprint 2025, arXiv:2504.13582. DOI: https://doi.org/10.48550/arXiv.2504.13582
  59. Yang, Y.; Han, J.; Liu, Z.; et al. Modeling and Adaptive Neural Network Control for a Soft Robotic Arm with Prescribed Motion Constraints. IEEE/CAA J. Autom. Sin. 2023, 10, 501–511.
  60. Mak, Y.X.; Naghibi, H.; Lin, Y.; et al. Adaptive Control of a Soft Pneumatic Actuator Using Experimental Characterization Data. Front. Robot. AI 2023, 10, 1056118.
  61. Lee, K.Y.; Park, S.-J.; Matthews, D.G.; et al. An Autonomously Swimming Biohybrid Fish Designed with Human Cardiac Biophysics. Science 2022, 375, 639–647.
  62. Zhang, Y.; Li, T.; Tao, H.; et al. Research on Adaptive Impedance Control Technology of Upper Limb Rehabilitation Robot Based on Impedance Parameter Prediction. Front. Bioeng. Biotechnol. 2024, 11, 1332689.
  63. Liu, X.; Ikemoto, S.; Yoshimitsu, Y.; et al. Learning Soft Robot Dynamics Using Differentiable Kalman Filters and Spatio-Temporal Embeddings. arXiv preprint 2023, arXiv:2308.09868. DOI: https://doi.org/10.48550/arXiv.2308.09868
  64. Hao, S.; Hu, L.; Liu, P.X. Second-Order Adaptive Integral Terminal Sliding Mode Approach to Tracking Control of Robotic Manipulators. IET Control Theory Appl. 2021, 15, 2145–2157.
  65. Tamang, L.; Bouadjenek, M.R.; Dazeley, R.; et al. Handling Out-of-Distribution Data: A Survey. IEEE Trans. Knowl. Data Eng. 2025, 37, 5948–5966.
  66. Hong, L.; Liu, L.; Peng, Z.; et al. Control of Marine Robots in the Era of Data-Driven Intelligence. Annu. Rev. Control Robot. Auton. Syst. 2025, 9, 22723–33729.
  67. Abbasi, E. The Impact of Climate Change on Travel-Related Vector-Borne Diseases: A Case Study on Dengue Virus Transmission. Travel Med. Infect. Dis. 2025, 65, 102841.
  68. Abbasi, E. The Impact of Climate Change on Aedes aegypti Distribution and Dengue Fever Prevalence in Semi-Arid Regions: A Case Study of Tehran Province, Iran. Sci. Environ. Res. 2025, 275, 121441.
  69. Wang, J.; Chortos, A. Control Strategies for Soft Robot Systems. Adv. Intell. Syst. 2022, 4, 2100165.
  70. Sajwan, M.; Singh, S. A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Collaborative Robot. Arch. Comput. Methods Eng. 2023, 30, 3489–3508.
  71. Wang, P.; Kershaw, J.; Russell, M.; et al. Data-Driven Process Characterization and Adaptive Control in Robotic Arc Welding. CIRP Ann. Manuf. Technol. 2022, 71, 45–48.
  72. RajaSekhar Reddy, N.V.; Liu, H.; Kim, T.; et al. Biohybrid–AI Marine Sensors for Ocean Health Monitoring under Climate Stress: A Systematic Review. Synthesis 2025, 3, 12–25.
  73. Kasaei, M.; Kouhkiloui Babarahmati, K.; Li, Z.; et al. A Data-Efficient Neural ODE Framework for Optimal Control of Soft Manipulators. Proc. 7th Conf. Robot Learn. 2023, 229, 2700–2713.
  74. Abbasi, E. Climate Change and Vector-Borne Disease Transmission: The Role of Insect Behavioral and Physiological Adaptations. Integr. Org. Biol. 2025, 7, obaf011.
  75. Zhao, W.; Peña Queralta, J.; Westerlund, T. Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: A Survey. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 1–4 December 2020.
  76. Banerjee, C.; Nguyen, K.; Fookes, C.; et al. A Survey on Physics-Informed Reinforcement Learning: Review and Open Problems. Expert Syst. Appl. 2025, 287, 128166.
  77. Abbasi, E. Global Expansion of Aedes Mosquitoes and Their Role in the Transboundary Spread of Emerging Arboviral Diseases: A Comprehensive Review. IJID One Health 2025, 6, 100058.
  78. Dulundu, A. The Future of Robotics: Integrating Biological Components in Biohybrid Robotics for Enhanced Functionality and Adaptability. Next Front. Life Sci. AI 2024, 8, 57.
  79. Tapia Sal Paz, B.; Sorrosal, G.; Mancisidor, A.; et al. Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly. Mathematics 2025, 13, 1120.
  80. Radha, H.M.; Hassan, A.K.A. A Survey on Bio-Signal Analysis for Human–Robot Interaction. Int. J. Electr. Comput. Eng. 2022, 12, 5998–6009.