AccScience Publishing / EIR / Online First / DOI: 10.36922/EIR025200005
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ORIGINAL RESEARCH ARTICLE

Data science in embodied artificial intelligence and robotics: A comprehensive study of models, methods, and applications

Sanjay Agal1* ,  Niyati Dhirubhai Odedra2
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1 Department of Artificial Intelligence and Data Science, Faculty of Engineering and Technology, Parul University, Vadodara, Gujarat, India
2 Department of Computer Science and Engineering, Dr V R Godhania College of Engineering and Technology, Porbandar, Gujarat, India
EIR, 025200005 https://doi.org/10.36922/EIR025200005
Received: 15 May 2025 | Revised: 4 September 2025 | Accepted: 8 September 2025 | Published online: 24 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Embodied intelligence stands at the confluence of robotics, machine learning, and cognitive science, promising systems that perceive, adapt, and act with context-aware reasoning. This study comprehensively analyzes recent advances in data-driven approaches that empower embodied agents with intelligent behaviors, focusing on hybrid models that integrate symbolic and sub-symbolic reasoning, multimodal robotic perception, and adaptive decision-making. We critically examine the role of data science in integrating deep learning, causal inference, and uncertainty handling across diverse robotic applications. Furthermore, the paper explores challenges in human-robot interaction, the ethical design of artificial intelligence, and the scalable deployment in real-world environments. By synthesizing interdisciplinary perspectives, we identify research gaps and propose a unifying roadmap to advance responsible, explainable, and autonomous systems. To demonstrate these concepts, we introduce the neuro-symbolic hybrid controller with adaptive fusion model, which fuses multimodal data using a transformer, extracts symbolic predicates, and applies differentiable reasoning for action selection. Experiments on MetaWorld, Yale-CMU-Berkeley, and real-world tasks achieved 92–96% success rates with robust sim-to-real transfer, outperforming proximal policy optimization, soft actor-critic, and behavioral cloning while maintaining low latency and interpretable decision-making. This work serves as a foundation for scholars and practitioners seeking to bridge theoretical insights with practical deployment of intelligent robotics.

Keywords
Embodied intelligence
Hybrid artificial intelligence
Robotic perception
Adaptive decision making
Human robot collaboration
Causal inference in artificial intelligence
Autonomous intelligent systems
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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