Machine Learning-Driven Polymer Composite Smart Skin for Integrated Sensing in Soft Robotic Systems

Year : 2026 | Volume : 14 | Special Issue 01 | Page : 121 136
    By

    Harish Reddy Gantla,

  • Radha Seelaboyina,

  • M. Sreenivasulu,

  • S. Sagar Imambi,

  • Sanjeev Kumar,

  • Muthukumar Subramanian,

  1. Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Telangana, India
  2. Associate Professor, Department of Computer Science and Engineering, Geethanjali College of Engineering & Technology, Hyderabad, Telangana, India
  3. Associate Professor, Department of Electrical and Communication Engineering, Kishkindha University, Mount View Campus, Balkari-Siruguppa Road, Karnataka, India
  4. Professor, Department of Computer Science and Engineering, Koneru Lakakshmaih Education and Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  5. Associate Professor, Department of Electrical and Communication Engineering, Aditya University, Surampalem, Andhra Pradesh, India
  6. Professor, Department of Computer Science and Engineering, Hindustan Institute of Technology & Science (Deemed to Be University), Chennai, Tamil Nadu, India

Abstract

Soft robotics has grown rapidly, but its progress is still constrained by the limitations of current sensing skins. Most polymer-based sensors provide either flexibility or sensitivity, yet they struggle to deliver real-time communication and adaptive intelligence when deployed in complex robotic environments. This disconnect between material performance and system-level responsiveness forms a critical bottleneck for practical deployment. Existing approaches often treat tactile sensing and wireless communication as separate problems. As a result, signal delays, noise accumulation, and restricted adaptability remain common, especially when robots must operate continuously in dynamic or human-interactive settings. To address this gap, we present a polymer-based smart skin that integrates carbon nanotube–graphene composites with a 6G-enabled ISAC module and a machine learning pipeline. The framework unifies sensing, communication, and adaptive control in a single stretchable layer. CNN–LSTM architectures were used to interpret tactile signals, while reinforcement learning optimized trade-offs between accuracy, capacity, and power efficiency. Experiments confirmed that the proposed skin achieved 94.7% recognition accuracy and maintained communication capacity above 22 Gbps under strain. Latency was reduced to below 6 ms, nearly halving values reported in earlier ISAC prototypes. Energy consumption fell by more than 20% through adaptive control. These results suggest that polymer composites, when embedded with ML-driven ISAC, can move beyond laboratory prototypes and into practical robotic systems. The work opens pathways toward low-latency, intelligent skins for soft robotics with implications for healthcare, collaborative manufacturing, and networked autonomous systems.

Keywords: 6G-enabled ISAC, CNN–LSTM, machine learning, nanocomposites, polymer-based smart skin, reinforcement learning, soft robotics, tactile sensing.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
Harish Reddy Gantla, Radha Seelaboyina, M. Sreenivasulu, S. Sagar Imambi, Sanjeev Kumar, Muthukumar Subramanian. Machine Learning-Driven Polymer Composite Smart Skin for Integrated Sensing in Soft Robotic Systems. Journal of Polymer & Composites. 2026; 14(01):121-136.
How to cite this URL:
Harish Reddy Gantla, Radha Seelaboyina, M. Sreenivasulu, S. Sagar Imambi, Sanjeev Kumar, Muthukumar Subramanian. Machine Learning-Driven Polymer Composite Smart Skin for Integrated Sensing in Soft Robotic Systems. Journal of Polymer & Composites. 2026; 14(01):121-136. Available from: https://journals.stmjournals.com/jopc/article=2026/view=235724


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Special Issue Subscription Original Research
Volume 14
Special Issue 01
Received 28/10/2025
Accepted 06/11/2025
Published 07/01/2026
Publication Time 71 Days
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