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All Journal INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Ahmad Dzaky
Universitas Pembangunan Panca Budi, Indonesia
Religion Humanities Social Sciences

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ADAPTIVE CONTROL STRATEGIES FOR ROBOTIC MANIPULATORS USING DEEP REINFORCEMENT LEARNING Irvan Malay; Muhammad Alfa Rozi; Ahmad Dzaky; Afif Arroofi; Mitgever Pandiangan; Haikal Lutfian Hsb; Febrio Ardly Saefsan
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 2 No. 10 (2025): AUGUST
Publisher : Adisam Publisher

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Abstract

This research examines adaptive control strategies for robotic manipulators using Deep Reinforcement Learning (DRL) through a systematic literature review approach. The main focus of the study is the identification of commonly used DRL algorithms, implementation challenges, and the direction of developing DRL-based adaptive control systems. The study results show that algorithms such as DDPG, SAC, and PPO are effective in addressing the non-linear dynamics and uncertainties of robotic manipulators, both in simulation and real-world environments. However, there are significant challenges such as the need for large training data, the simulation-to-real gap, and the limitations in the interpretability of control policies. The integration of hybrid control strategies, the development of more sample-efficient algorithms, and the application of hierarchical and meta-reinforcement learning have been identified as promising future research directions. This study provides a foundation for the development of more flexible, efficient, and safe robotic control systems to support various industrial applications.
Co-Authors Afif Arroofi Febrio Ardly Saefsan Haikal Lutfian Hsb Irvan Malay Mitgever Pandiangan Muhammad Alfa Rozi