Soft robotic systems have emerged as a transformative paradigm within robotics research, driven by their intrinsic compliance, adaptability, and safety in unstructured and human-centered environments. Unlike traditional rigid-bodied robots, soft robots exploit deformable materials, bioinspired architectures, and distributed actuation to achieve complex behaviors that are otherwise difficult to realize using classical mechanical designs. Recent advances have further accelerated this field through the convergence of soft materials science, intelligent control, artificial perception, and data-driven learning frameworks. Within this evolving landscape, precision programming of Mult responsive soft robots remains a central scientific and engineering challenge. The need to achieve predictable, repeatable, and decoupled responses across multiple stimulus such as magnetic fields, mechanical contact, and environmental constraints—has motivated novel approaches that unify material design and control logic.

This article presents an extensive theoretical and analytical investigation into the foundations, methodologies, and implications of single-step precision programming for decoupled multiresponsive soft robotic systems, with particular emphasis on millirobot-scale platforms. Building upon recent breakthroughs in precision programming of soft millirobots (Zheng et al., 2024), the paper situates these developments within a broader scholarly context that includes bioinspired mechanoreception, flexible and endoluminal robotic systems, human–robot interaction, multi-agent learning, and intelligent sensing. Rather than treating control, perception, and embodiment as separate problems, the article advances the argument that future soft robotic intelligence must be understood as an integrated property emerging from material computation, adaptive control strategies, and environment-aware learning.

The methodology adopted in this work is interpretive and theory-driven, synthesizing insights across robotics, intelligent systems, and design theory. Through detailed textual analysis, the paper examines how single-step programming frameworks reduce system complexity, mitigate control coupling, and enable scalable deployment of soft robots in constrained environments. The results section articulates emergent patterns and conceptual findings grounded in existing literature, highlighting how precision programming reshapes performance, reliability, and task generalization. The discussion expands these findings through critical comparison with alternative paradigms, addresses unresolved limitations, and outlines future research trajectories, including ethical, clinical, and industrial implications. By offering a deeply elaborated and publication-ready contribution, this article aims to serve as a comprehensive reference for researchers and practitioners seeking to understand and advance the next generation of intelligent soft robotic systems.

Soft robotics, precision programming, multiresponsive systems

Kim, J.; de Mathelin, M.; Ikuta, K.; Kwon, D.-S. Advancement of Flexible Robot Technologies for Endoluminal Surgeries. Proc. IEEE 2022, 110, 909–931.

Kasowaki, L.; Kooper, J. Synergizing AI and Big Data: a Futuristic Approach to Data Management. EasyChair, 2024.

Zhang, L.; Cai, Z.; Yan, Y.; Yang, C.; Hu, Y. Multi-agent policy learning-based path planning for autonomous mobile robots. Eng. Appl. Artif. Intell. 2024, 129, 107631.

Gong, S.; Ding, Q.; Wu, J.; Li, W.-B.; Guo, X.-Y.; Zhang, W.-M.; Shao, L. Bioinspired Multifunctional Mechanoreception of Soft–Rigid Hybrid Actuator Fingers. Adv. Intell. Syst. 2022, 4, 2100242.

Zheng, Z.; Han, J.; Shi, Q.; Demir, S.O.; Jiang, W.; Sitti, M. Single-step precision programming of decoupled multiresponsive soft millirobots. Proc. Natl. Acad. Sci. USA 2024, 121, e2320386121.

McCorduck, P. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. AK Peters/CRC Press, 2004.

Liu, Z.; Liu, J.; Wang, H.; Yu, X.; Yang, K.; Liu, W.; Nie, S.; Sun, W.; Xie, Z.; Chen, B.; et al. A 1 mm-Thick Miniatured Mobile Soft Robot with Mechanosensation and Multimodal Locomotion. IEEE Robot. Autom. Lett. 2020, 5, 3290–3297.

Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J.W.; Wallach, H.; Iii, H.D.; Crawford, K. Datasheets for datasets. Commun. ACM 2021, 64, 86–92.

Su, H.; Di Lallo, A.; Murphy, R.R.; Taylor, R.H.; Garibaldi, B.T.; Krieger, A. Physical human–robot interaction for clinical care in infectious environments. Nat. Mach. Intell. 2021, 3, 184–186.

Zhang, H.; Yi, H.; Wang, C.; Yang, J.; Jin, T.; Zhao, J. A Robotic Microforceps for Retinal Microsurgery with Adaptive Clamping Method. IEEE/ASME Trans. Mechatron. 2024, 29, 4492–4503.

Wang, M.; Yan, Z.; Wang, T.; Cai, P.; Gao, S.; Zeng, Y.; Wan, C.; Wang, H.; Pan, L.; Yu, J.; et al. Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors. Nat. Electron. 2020, 3, 563–570.

Singla, A.; Padakandla, S.; Bhatnagar, S. Memory-based deep reinforcement learning for obstacle avoidance in UAV with limited environment knowledge. IEEE Trans. Intell. Transp. Syst. 2019, 22, 107–118.

Yue, Y.; Wen, M.; Putra, Y.; Wang, M.; Wang, D. Tightly-Coupled Perception and Navigation of Heterogeneous Land-Air Robots in Complex Scenarios. Proc. IEEE Int. Conf. Robot. Autom. 2021, 10052–10058.

Kochanowska, M.; Gagliardi, W.R. The double diamond model: In pursuit of simplicity and flexibility. Perspectives on Design II: Research, Education and Practice, 2022, 19–32.

Apple. Privacy: Built into Everything We Do. Apple Inc., 2020.

GDPR. General Data Protection Regulation. 2018.

Ghorbani, M.A. AI Tools to Support Design Activities and Innovation Processes. 2023.

Bosch, P. The Edge: Smart and Sustainable Building. J. Sustain. Archit. 2016, 8, 23–35.

Patel, K. Big Data in Finance: An Architectural Overview. Int. J. Comput. Trends Technol. 2023, 71, 61–68.

Gomez-Uribe, C.A.; Hunt, N. The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 2015, 6, 1–19.

Anh, P.T.Q.; Thuyet, D.Q.; Kobayashi, Y. Image classification of root-trimmed garlic using multi-label and multi-class classification with deep convolutional neural network. Postharvest Biol. Technol. 2022, 190, 111956.

Xia, G.; Zhang, L.; Dai, Y.; Xue, Y.; Zhang, J. Sound Feedback Fuzzy Control for Optimizing Bone Milling Operation During Robot-Assisted Laminectomy. IEEE Trans. Fuzzy Syst. 2024, 32, 2341–2351.