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Setyawan Wibisono
Universitas Stikubank, Semarang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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A Hybrid NeuralSymbolic Approach for Human Robot Interaction Enhancement Using Multimodal Sensor Fusion and Context Aware Behavioral Adaptation Techniques Setyawan Wibisono; Hayadi Hamuda; Encik Yoega Renaldi
Intelligent Systems and Robotics Vol. 1 No. 1 (2026): February: Intelligent Systems and Robotics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/isr.v1i1.35

Abstract

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.
Co-Authors Ade Izyuddin Aditya Wafda Nahari Adriyansyah Juri Setyo Wibowo Aji Supriyanto Almin, Imam Husni Al Amanaturohim, Afif Anton Setiawan Aris Munandar, Muhammad Arjuanda, Resa Cahyono, Adi Tri Cahyono, Taufik Dwi Darmawan, Rico dirgantara krisna gaesa Eddy Nurraharjo Edwin Setiawan Edy Supriyanto Edy Winarno Eko Nur Wahyudi Elen Puspitasari Encik Yoega Renaldi Endang Lestariningsih Enty Nur Hayati Fariz Setiawan Fatkhul Amin Fatkhul Amin Hardiyanti, Susi Sri Hayadi Hamuda Heribertus Yulianton Herny Februariyanti Herny Februariyanti Hizkia Indra Purwanto Huda Damar Sangkara Imam Husni Al Amin Indriani Indriani Irfan, Irfan Dwi Yulianto Istiyawan, Diyan Bagus Jati Sasongko Wibowo Jeffri Alfa Razaq Juniko Dwiki Saputro Kukuh Tri Nur Iman Kurniahadi, Wiwien Majid, Dzikri Fauzul Mardi Siswo Utomo Moch Mustofa Muhammad Aris Munandar Muhammad Yusan Al Anshari Muji Sukur Nur Cahyo IA, Nur Cahyo IA Nur Kharisa Umami Okka Hermawan Yulianto Rizki, Tita Saeffurrohman . Sahrul Ade Amanatulloh Sambora, Rendy Gibson Setyono, Setyono Siti Munawaroh Sri Eniyati Sulistyawan, Perdana Eka Putra Taufiq Dwi Cahyono Trisulo, Didik Umam, Muhammad Khoirul Wiwien Hadikurniawati zunaidi, edi