Advanced Robotic Learning Models Using Artificial Intelligence

Alexander Co Abad

Advanced Robotic Learning Models Using Artificial Intelligence

Author(s)	Dr. Alexander Co Abad
Country	Philippines
Abstract	Artificial intelligence has significantly transformed robotic learning by enabling robots to acquire knowledge, recognize patterns, adapt to changing environments, and improve task performance through data-driven mechanisms. Advanced robotic learning models integrate machine learning, deep learning, reinforcement learning, imitation learning, representation learning, computer vision, sensor fusion, and intelligent decision-making to develop more autonomous robotic systems. This study examines advanced robotic learning models using artificial intelligence, with particular emphasis on learning architectures, perception, adaptive behavior, reinforcement-based decision-making, and applications in intelligent robotics. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis demonstrates that AI-based learning models can improve robotic perception, manipulation, navigation, planning, prediction, and task adaptation. Deep learning enables robots to extract complex patterns from sensory information, while reinforcement learning supports sequential decision-making through interaction with environments. Imitation learning can allow robots to acquire behaviors from demonstrations, reducing the need for manually programmed control strategies. However, practical implementation remains affected by data requirements, computational complexity, transfer limitations, safety concerns, model interpretability, sensor uncertainty, and real-world learning costs. The study concludes that advanced robotic learning requires integration between artificial intelligence, perception, control, planning, and continuous feedback. Future research should focus on efficient learning architectures, safe reinforcement learning, multimodal perception, simulation-to-real transfer, continual learning, explainable AI, foundation models, and adaptive robotic systems capable of generalizing learned knowledge across tasks and environments.
Keywords	Robotic Learning, Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Imitation Learning, Autonomous Robotics, Robot Perception, Intelligent Systems, Adaptive Robotics.
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-12-27

View / Download PDF File

About AIJARD Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijard.com

American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Advanced Robotic Learning Models Using Artificial Intelligence

Share this