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Embodied Artificial Intelligence

Concepts, Challenges, and Applications
Edited by R. Rajmohan, Subrata Chowdhury, G. Glorindal Selvam, T. Ananth Kumar, and S. Balamurugan
Series: Leading-Edge Breakthroughs in Artificial Intelligence
Copyright: 2026   |   Status: Published
ISBN: 9781394404353  |  Hardcover  |  
512 pages
Price: $245 USD
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One Line Description
Bridging the gap between abstract algorithms and physical reality, this essential volume equips researchers and professionals with the technical depth and forward-looking insights needed to pioneer the next generation of robotics, smart environments, and seamless human-machine collaboration.

Audience
Academics, researchers, AI engineers, robotics developers, environmental technologists, and policymakers in computer science, human–computer interaction, and intelligent systems.

Description
Embodied artificial intelligence represents a significant evolution in the field of intelligent systems, where algorithms are no longer confined to virtual spaces but are integrated with physical bodies, sensors, and real-world contexts. Unlike traditional AI, which primarily processes abstract data, embodied AI emphasizes perception, interaction, and adaptation in dynamic environments, making it a cornerstone for next-generation applications in robotics, IoT, and human–machine collaboration. Bringing together advances in robotics, IoT, human–computer interaction, and machine learning, this book highlights how embodied intelligence is reshaping the way we live, work, and engage with technology. From enhancing personalized assistance in smart homes to enabling adaptive infrastructure in smart environments, embodied AI is setting the stage for the next generation of intelligent living. The book examines how embodied AI is transforming industries and societies alike. In marketing, it drives immersive consumer experiences through interactive, human-like engagement, while in smart cities, it fosters sustainable development, intelligent mobility, and participatory urban governance. Balancing technical depth with practical relevance, the chapters provide readers with both foundational insights and forward-looking perspectives on the opportunities and challenges of embodied AI. Designed for academics, researchers, and professionals, this volume serves as a reference for understanding how intelligent systems are blending seamlessly into human contexts.
Readers will find the volume:
• Explores how embodied AI is driving innovation in smart homes, smart environments, marketing, and smart cities;
• Connects AI, robotics, IoT, and human–computer interaction for broad academic and professional audiences;
• Highlights cutting-edge use cases and trends that shape sustainable and intelligent living;
• Serves as both a reference for researchers and a guide for practitioners seeking real-world applications.

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Author / Editor Details
R. Rajmohan, PhD is an Assistant Professor at the SRM Institute of Science and Technology’s Kattankulathur Campus, Tamil Nadu, India. He has published more than 60 papers in various reputed journals, 20 book chapters, six edited books, one authored book, and three patents. His fields of interest include artificial intelligence, data science, medical imaging, machine learning, wireless networks, deep learning, and IoT.

Subrata Chowdhury, PhD is an Associate Professor in the Department of Computer Science and Machine Learning, Sreenivasa Institute of Technology and Management Studies, Chittoor, India. He has four books and more than 100 research papers, copyrights, and patents to his credit, in addition to his extensive work as an editor. He has handled various projects in AI, blockchain, and cloud computing for national and international clients.

G. Glorindal Selvam, PhD is a is a highly experienced and accomplished academic and computer engineering professional with over 20 years of expertise and is recognized for her leadership in both academic and research roles. Her professional highlights include patent publications in fields such as IoT, cancer detection, and agricultural technology, alongside numerous awards for innovation. Her diverse roles reflect his commitment to academic excellence, research, and collaboration with institutions worldwide.

T. Ananth Kumar, PhD is an Associate Professor and Head of Research at the IFET College of Engineering, Tamil Nadu, India. He has published one authored and six edited books, as well as many book chapters, patents, and articles in international journals and conferences. His fields of interest include networks on chips, computer architecture, and application-specific integrated circuit design.

S. Balamurugan, PhD is the Director of Research, iRCS, an Indian Technological Research and Consulting Firm. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups.

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Table of Contents
Series Preface
Preface
Acknowledgments
Part I: Foundations and Frontiers of Embodied Artificial Intelligence
1. Key Components of Neural Models for Embodied AI
P. Deivendran, G. Arun Sampaul Thomas, S. Muthukaruppasamy, J. Nandha Gopal and R. Senthilkumar
1.1 Introduction
1.2 Agent Architecture and Embodied Neural Agents
1.3 Methods and Task Models
1.3.1 Task Environment
1.4 Multi-Agent Simulations
1.4.1 Single-Agent Simulations
1.5 Methods
1.6 Simulated Agent Design
1.6.1 Single Agent Gradient
1.6.2 Multi Agent Gradient
1.7 Performance Analysis
1.8 Conclusion
References
2. Energy Efficiency in Embodied AI Systems
J. Dhanalakshmi, Aryan Raj, Aryan Nair, Chin-Shiuh Shieh and A. Prabhu Chakkaravarthy
2.1 Introduction
2.1.1 Overview of Embodied AI
2.1.2 Importance of Energy Efficiency in Embodied AI
2.1.3 Scope and Objectives of the Study
2.2 Energy Consumption Challenges in Embodied AI
2.2.1 High Computational Demands and Power Usage
2.2.2 Role of Sensors, Processors, and Actuators in Power Consumption
2.2.3 Trade-Offs between Performance and Energy Efficiency
2.2.4 Different Strategies Maximize This Balance
2.3 Hardware Optimization for Energy-Efficient Embodied AI
2.3.1 Specialized Processors for AI Workloads
2.3.2 Neuromorphic Computing for Power Reduction
2.3.3 Low-Power Sensors and Actuators
2.3.3.1 Sensors with Energy Efficiency
2.3.3.2 Energy-Effective Actuators
2.3.4 Energy Harvesting Technologies
2.3.4.1 Techniques for Energy Harvesting in Artificial Intelligence Systems
2.4 Software Innovations for Energy Efficiency
2.4.1 AI Model Compression Techniques
2.4.2 Quantization and Pruning in Deep Neural Networks
2.4.3 Adaptive Algorithms for Dynamic Energy Management
2.4.4 Distributed and Edge Computing Strategies
2.4.4.1 Edge Computing for Energy Efficiency
2.4.4.2 Optimizing AI Workloads with Distributed Systems
2.5 Architectural Enhancements for Power Management
2.5.1 Modular System Architectures
2.5.2 Cloud Offloading and Distributed Processing
2.5.3 Low-Power Communication Protocols
2.5.4 Methods for Maximizing Energy in Artificial Intelligence Transmission
2.6 Recent Advancements and Case Studies
2.6.1 Corki: Algorithm-Architecture Co-Design for Embodied AI Robots
2.6.2 Greenlighting: Decoupling Structural and Quantitative Knowledge
2.6.3 Neuromorphic Hardware for AI Data Centers
2.6.4 Energy-Efficient AI in Edge and IoT Systems
2.7 Results and Discussion
2.8 Future Directions in Energy-Efficient Embodied AI
2.8.1 Emerging AI Algorithms for Low-Power Computing
2.8.2 Sustainable and Green Computing Trends
2.8.3 Potential Breakthroughs in Hardware and Software Co-Design
2.9 Conclusion
2.9.1 Summary of Key Findings
2.9.2 Implications for Industry and Research
2.9.3 Final Thoughts on Sustainability in Embodied AI
References
3. Embodied AI in Healthcare: From Clinical Applications to Assistive Living
N. A. Natraj and P. Ashok
3.1 Introduction
3.1.1 Overview of Embodied AI’s Role in Healthcare and Assistive Technologies
3.1.2 Current Challenges in Healthcare Automation and Patient Assistance
3.1.3 Importance of Physical Interaction in Medical and Assistive Applications
3.2 Foundations of Healthcare Robotics
3.2.1 Sensing and Perception in Medical Environments
3.2.2 Physical Interaction Requirements for Patient Care
3.2.3 Safety Considerations and Regulatory Frameworks
3.2.4 Integration with Existing Medical Systems and Workflows
3.3 Applications in Clinical Settings
3.3.1 Surgical Assistance and Robotic Surgery
3.3.2 Patient Monitoring and Vital Sign Measurement
3.3.3 Physical Therapy and Rehabilitation Robotics
3.3.4 Hospital Logistics and Automated Material Handling
3.3.5 Infection Control and Sanitization Robots
3.4 Assistive Robotics for Daily Living
3.4.1 Mobility Assistance and Smart Wheelchairs
3.4.2 Personal Care Robots for Activities of Daily Living
3.4.3 Home Monitoring and Fall Prevention Systems
3.4.4 Social Assistance Robots for Elderly Care
3.4.5 Cognitive Support Systems for Patients with Disabilities
3.5 Future Directions and Challenges
3.5.1 Ethical Considerations in Healthcare Robotics
3.5.2 Human-Robot Trust and Acceptance in Medical Settings
3.5.3 Integration of AI with Embodied Systems
3.5.4 Privacy and Security Considerations
3.5.5 Emerging Technologies and Their Potential Impact
References
4. Embodied AI for Human Robot Collaboration Using Agentic
AI and Attention Economy
Robin Rohit Vincent, Nisha F., Varalaskhmi T. and Anand Muthukumarappan
4.1 Introduction
4.2 Fundamentals of Embodied AI
4.2.1 Definition and Characteristics
4.2.2 Evolution of Human-Robot Collaboration
4.3 Agentic AI: Empowering Autonomous Collaboration
4.3.1 Concept of Agentic AI
4.3.2 Challenges and Ethical Considerations
4.4 Attention Economy and Human-Robot Interaction
4.4.1 The Role of Attention in AI Systems
4.4.2 Attention Optimization in Human-Robot Collaboration
4.5 System Architecture and Implementation
4.5.1 System Overview
4.5.2 Hardware and Software Platforms
4.5.2.1 Hardware Components
4.5.2.2 Software Frameworks
4.6 Methodology
4.6.1 Perception Layer
4.6.2 Agentic Decision-Making
4.6.3 Attention Modeling
4.6.4 Experimental Setup
4.6.5 Code Implementation
4.6.6 Performance Evaluation and Benchmarking
4.6.7 Result Analysis and Comparison with Existing Systems
4.7 Applications and Case Studies
4.7.1 Healthcare
4.7.2 Manufacturing and Industry 4.0
4.7.3 Education and Assistive Learning
4.7.4 Disaster Management and Defense
4.8 Limitations
4.9 Future Directions and Ethical Implications
4.9.1 Enhancing Ethical AI Governance
4.9.2 Advances in Multimodal AI for Enhanced Perception
4.9.3 Societal and Psychological Considerations
4.10 Summary
References
5. Enhancing Urban Intelligence Through Adaptive Systems and Embodied AI
D. Devi Aruna, Prativa Mishra, B. Ramya, P. Dinesh Kumar and C. Kumuthini
5.1 Introduction of Smart Cities and IoT
5.1.1 Role of AI in Urban Intelligence
5.1.2 Introduction to Embodied AI
5.1.3 Challenges in Current Smart City Implementations
5.2 Literature Review
5.2.1 Evolution of Smart Cities and IoT
5.2.2 AI Applications in Smart Cities: Past and Present
5.2.3 Embodied AI: Definition and Key Concepts
5.2.4 Role of Machine Learning and Deep Learning in Smart Cities
5.3 Hybrid AI Model for Smart City Adaptation
5.3.1 Need for Hybrid AI Approaches in Smart Cities
5.3.2 Challenges in Smart City AI Implementation
5.4 Methodology
5.4.1 Proposed Multi-Agent Hybrid Model (CMA-HM)
5.4.2 Cognitive AI Layer
5.4.3 Adaptive Control Layer
5.4.4 Federated Learning for Decentralized Processing
5.4.5 Reinforcement Learning for Real-Time Adaptation
5.4.6 Graph Neural Networks (GNNs) for Urban Spatial Intelligence
5.4.7 Multi-Agent Deep Reinforcement Learning (MADRL) for Smart Traffic Management
5.4.8 Quantum-Inspired Optimization for City Resource Allocation
5.4.9 Result Analysis and Comparison with Existing Systems
5.5 Conclusion
Bibliography
Part II: Techniques in Embodied Artificial Intelligence
6. Real-Time Sign Language Translator with Dynamic Ensemble Learning Using Embodied Artificial Intelligence
Ganesh Khekare, Pranay Batta, Tavishi Jain, Vincent Omollo Nyangaresi, Nischal Puri and Seema Raut
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.3.1 Technical Feasibility
6.3.2 Economic Convenience
6.3.3 Social Feasibility
6.3.4 Data Flow Diagram
6.3.5 Class Diagram
6.3.6 Project Plan
6.4 Results and Discussion
6.4.1 Sign Language to Text to Speech
6.4.2 Frontend for the App on Flutter
6.5 Conclusion
6.6 Future Scope
References
7. Cognitive Neural Systems for Embodied Artificial Intelligence in Skin Disease Detection and Classification Framework
Karthick Manoj R., S. Aasha Nandhini and M. Batumalay
7.1 Introduction
7.2 Literature Survey
7.3 Proposed Methodology
7.3.1 Image Data Preprocessing and Augmentation
7.3.2 Features Extraction
7.3.3 Weighted Average Ensemble Learning Classifications
7.3.4 Interpretability of XAI Models
7.4 Results and Discussion
7.4.1 Performance Assessment
7.4.2 Explainable Artificial Intelligence (XAI) Integration
Using Grad-CAM
7.5 Conclusion
References
8. Enhanced AI-Powered Optical Character Recognition with CNN and Graph Theory-Based Arabic Handwriting Segmentation
N. Nithiyanandam, N. Sivaranjani, R. Madhura and G. Malarselvi
8.1 Introduction
8.2 Segmentation Contribution to AI-OCR
8.2.1 An Approach to Arabic AI-OCR
8.2.2 Segmentation of Arabic Handwriting Based on Graph Theory
8.2.3 Empowering AI-OCR with Convolutional Neural Networks and Graph Theory
8.3 Methodology
8.3.1 Preprocessing Stage
8.3.2 Over-Segmentation Stage
8.3.3 Generation of Over-Segmented Picture Graphs
8.3.3.1 Establishing an Adjacency Matrix – Contacting Nodes
8.3.3.2 Assignment of Secondary Parts to the Primary Parts
8.3.4 WSW Extraction
8.3.5 The Creation of Graphs for Hypothesis Segmentation
8.3.6 Validation of Segments Using CNN
8.4 Implementation and Discussion of Results
8.4.1 Dataset
8.4.2 Segmentation from Word to Sub-Word
8.4.3 Segmentation of Sub-Words into Characters
8.5 Conclusion
References
9. A Novel XAI-Based Deep Learning Approach for Accurate
Diagnosis of Knee Osteoarthritis
Lavanya R. and Sudha Senthilkumar
9.1 Introduction
9.2 Symptoms and Progression of KOA
9.3 Traditional Diagnostic Approaches for KOA
9.3.1 Clinical Assessments
9.3.2 Radiographic Evaluations
9.3.3 Limitations of X-Ray and Manual MRI Analysis
9.4 Artificial Intelligence’s Function in Medical Imaging
9.4.1 Explainable AI for Medical Trust
9.5 Transformer-Based Explainable AI (XAI) for KOA Detection
9.5.1 Vision Transformers (ViTs) in KOA Detection
9.5.2 Recognizing Deep Learning Transformer Models
9.5.3 Mechanisms of Attention Emphasizing Important Imaging Features
9.6 Methods
9.6.1 Dataset Description and Preprocessing
9.6.2 Image Preprocessing
9.6.3 Model Architecture and Training Process
9.6.4 Integration of Attention Mechanisms for Explainability
9.7 Performance Evaluation and Comparative Analysis
9.7.1 Comparison with Conventional Deep Learning Models
9.7.2 Table for Performance Metrics Comparison
9.8 Benefits of AI-Powered KOA Diagnosis
9.9 Ethical Considerations and Challenges in AI-Based Diagnosis
9.10 Future Directions and Innovations in Transformer-Based
Explainable AI for Knee Osteoarthritis Diagnosis
9.11 Conclusion
References
10. Artificial Intelligence-Based Monetary Administration in Institutions Employing Cognitive Fuzzy Logic and Sensors
Gokulnath C. and Shibu N. V.
10.1 Introduction
10.1.1 Mathematical Techniques for Enhanced Financial Decision-Making
10.1.2 The Advantages of AI-Powered Educational Financing Systems
10.2 Literature Survey
10.3 Proposed Method
10.3.1 AI Embodied Fuzzy Evaluation-Based Monetary Administration System (AIembFE-MAS)
10.3.2 Artificial Intelligence Embodied Fuzzy System
10.3.3 Mathematical Modeling (MM)
10.4 Findings and Analysis
10.4.1 The AIembFE-MAS System’s Proposed Accuracy Ratio in Percent
10.4.2 Ratio for Making Decisions (%)
10.4.3 The Ideal Percentage for Distributing Capital
10.4.4 The Fcm-MAS System-Based Educational Institution Performance Ratio
10.5 Conclusion
References
11. Applying AI Embodied Deep Learning Models for Real-Time Prediction and Classification of Dengue Virus Serotypes Using Complete Genome Data
N. Sivaranjani, N. Nithiyanandam, V. Manickavasagam, G.K. Sandhia and S. Nikkath Bushra
11.1 Introduction
11.1.1 Objectives of the Study
11.2 Resources and Procedures
11.2.1 Gathering and Preprocessing Data
11.2.2 The AIemDLDVE Framework and Its Application to Sequence Creation
11.2.2.1 Comparisons of Generative Models
11.2.2.2 ConV1d
11.2.2.3 GRU
11.2.2.4 A Fundamental Recurrent Neural Network
11.2.3 The AIemDLDVE Framework and Its Application to Sequence Classification
11.3 Discussion and Results
11.4 Conclusion
References
Part III: Applications of Embodied Artificial Intelligence
12. Embodied Artificial Intelligence for Smart Homes: Towards
Adaptive and Context-Aware Automation
D. Divya, M. Kalaimani, C.P. Jetlin and K. Kavitha
12.1 Introduction
12.1.1 Problem Statement
12.1.2 Motivation
12.2 Materials and Methods
12.3 Framework of IoT-Enabled Smart City
12.3.1 AI on Smart City Technologies
12.4 Home Automation
12.4.1 Implementation and Sensors
12.4.2 Discussion
12.5 General Applications of Smart Cities
12.6 Smart City Security Challenges
12.7 Conclusion
References
13. Smart Umbrella: An Embodied AI-Powered Forecasting Companion for Rainy Days
Nivethitha V., G. Achuth, Dinesh R. and Lakshmanan L.
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.3.1 Data Collection
13.3.2 Data Preprocessing
13.3.3 Proposed Architecture
13.3.4 System Design and Operation
13.4 Algorithm Design
13.5 Workflow
13.5.1 Rain Prediction and Processing
13.5.2 Communication and User Notification
13.5.3 System Adaptation and Optimization
13.6 Use Cases
13.7 Evaluation Results
13.8 Conclusion
Bibliography
14. TactiSense: An IoT-Based Skin Cancer Detection System
Using Machine Learning and Skin Elasticity Analysis
Arumuga Arun R. and Aniket Anand
14.1 Introduction
14.1.1 Background
14.1.2 Problem Statement
14.2 Literature Review
14.2.1 Limitations of Traditional Diagnostics
14.2.2 Skin Cancer Detection Techniques Using AI and Deep Learning
14.2.3 Biomechanics Properties and Multimodal Diagnostics
14.2.4 Technologies for Skin Elasticity Measurement
14.2.5 IoT and Edge Computing for Diagnostics for All
14.2.6 Challenges, Ethical Considerations, and Regulatory Landscape
14.3 Methodology
14.3.1 Fundamental Biomechanics of Skin
14.3.2 Altered Mechanical Properties in Malignant Tissues
14.3.3 Elasticity Measurement
14.3.4 Machine Learning and Image Analysis
14.4 System Architecture
14.4.1 Hardware Design
14.4.2 Software Workflow
14.5 Implementation
14.5.1 Elastimeter
14.5.2 Preprocessing
14.5.3 Transfer Learning
14.5.4 Model Performance Metrics
14.5.5 Confusion Matrix Analysis
14.5.6 Model Training and Validation
14.5.7 Model Interpretability
14.5.8 Deploying on Edge Devices with TensorFlow Lite
14.6 Results
14.7 Discussion
14.8 Conclusion
References
15. The Future of Embodied AI in Smart Cities and IoT
Manas Kumar Yogi and Subhasri Telkar
15.1 Introduction
15.1.1 The Smart City Vision: Current Landscape and Limitations
15.1.2 Embodied AI: A New Paradigm for Urban Innovation
15.1.3 System Designer’s Perspective: Key Considerations
15.2 Building Blocks of Embodied AI Systems for Smart Cities
15.2.1 Agent Architectures: Physical vs. Virtual Embodiment
15.2.1.1 Micro-Robots for Infrastructure Maintenance
15.2.1.2 Humanoid Robots for Citizen Assistance
15.2.1.3 Virtual Avatars for Information Dissemination
15.2.2 Perception and Localization: Understanding the Urban Environment
15.2.2.1 Data Fusion and Contextual Awareness
15.2.3 Navigation and Path Planning: Moving through the City
15.3 Human-Robot Interaction (HRI) in Urban Environments: A Hybrid Model
15.3.1 A Hybrid Interaction Model for Urban HRI
15.3.1.1 Layered Communication Architecture: Physical, Verbal, Informational
15.3.1.2 Dynamic Modality Selection: Context and User-Driven
15.3.1.3 Contextual Awareness and Personalization
15.3.2 Key Components of the Hybrid Model
15.3.2.1 Advanced Natural Language Processing (NLP)
15.3.2.2 Expressive Robot Gestures and Proxemics
15.3.2.3 Augmented Reality (AR) Interfaces
15.3.2.4 User-Adaptive Interaction Strategies
15.3.3 Evaluating the Hybrid Interaction Model
15.3.3.1 User Studies
15.3.3.2 Performance Metrics
15.3.3.3 Real World Deployments
15.4 Results
15.5 Research Roadmap and Future Directions
15.5.1 Research Gaps in Embodied AI for Smart Cities and IoT
15.5.2 Visualizing the Future of Embodied AI in Smart Cities
15.5.3 Call for Cooperation: Encouraging Interdisciplinary Research
15.6 Conclusion
References
16. Embodied AI in Marketing: The Rise and Impact of AI Influencers
Rishi Satapathy and Abhishek Kumar
16.1 Introduction
16.1.1 Embodied AI – The Next Generation of Artificial Intelligence
16.1.2 Virtual Influencers: Next Steps toward Influencer
Marketing
16.1.3 Significance of Embodied AI in Marketing
16.1.4 Purpose and Scope of This Chapter
16.2 Methodology
16.2.1 Conceptual Framework Development
16.2.2 Data Collection Method
16.2.3 Data Analysis Technique
16.3 Background and Evolution of Virtual Influencers
16.3.1 History of Virtual Influencing
16.3.2 Technological Advancements Enabling Embodied AI
16.3.3 Comparison with Human Influencers
16.4 Creation and Maintenance of Virtual Influencers
16.4.1 Concept Design and Character Development
16.4.2 3D Modeling and CGI Animation
16.4.3 AI Integration and Social Media Deployment
16.5 The Rise of Virtual Influencers
16.5.1 Growth Trends and Market Penetration
16.5.2 Platforms and Industries Leveraging Virtual Influencers
16.6 Impact on Consumer Behavior and Brand Loyalty
16.6.1 Influence on Consumer Purchase Decisions
16.6.2 Emotional Engagement and Trust Factors
16.6.3 Impact on Brand Perception and Loyalty
16.7 Marketing Strategies Involving Virtual Influencers
16.7.1 Brand Collaborations
16.7.2 Ethical Considerations and Authenticity
16.7.3 Content Creation and Storytelling
16.7.4 Data Analytics and Optimization
16.8 Challenges and Ethical Considerations
16.8.1 Authenticity and Transparency
16.8.2 Regulatory and Legal Aspects
16.8.3 Advertising Disclosures and Endorsement Regulations
16.8.4 Jurisdictional Challenges and Global Regulations
16.8.5 Ethical and Social Implications
16.8.6 Future Directions and Recommendations
16.9 Future Trends and Implications
16.9.1 Progress in Embodied AI
16.9.2 Long-Term Marketing Impact
16.9.3 Role in Emerging Digital Spaces
16.9.4 Regulatory and Legal Implications
16.9.5 Future Prospects and Strategic Implications
16.9.6 Theoretical Model
16.10 Conclusion
16.10.1 Significance in Marketing
16.10.2 Final Thoughts
16.10.3 Limitations of the Chapter
16.10.4 Future Scope
References
Index

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