STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

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

  1. Harish Reddy Gantla Department of Computer Science and Engineering, Vignan Institute of Technology and Science
  2. Radha Seelaboyina Department of Computer Science and Engineering, Geethanjali College of Engineering & Technology, Hyderabad
  3. M. Sreenivasulu Department of Electrical and Communication Engineering, Kishkindha University, Mount View Campus, Balkari-Siruguppa Road
  4. S. Sagar Imambi Department of Computer Science and Engineering, Koneru Lakakshmaih Education and Foundation, Vaddeswaram, Guntur
  5. Sanjeev Kumar Department of Electrical and Communication Engineering, Aditya University, Surampalem
  6. Muthukumar Subramanian Department of Computer Science and Engineering, Hindustan Institute of Technology & Science (Deemed to Be University), Chennai

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

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