A Comprehensive Survey of Deep Reinforcement Learning Techniques for Soft Mobile Robots

ABSTRACT: Soft robotics has emerged as a promising direction for enabling safe, adaptive, and energy-efficient interactions with unstructured environments due to its inherent compliance. Recently, Deep Reinforcement Learning (DRL) has become a powerful tool for autonomous behavior generation in soft robots, surpassing limitations of classical model-based control. However, despite rapid growth of publications in this domain, there is still a lack of systematic comparative surveys that clarify how different DRL approaches have been used for soft mobile robots, what types of tasks they address, and what performance evaluation criteria have been used. In this article, we review and classify existing works in DRL-enabled soft robotics, focusing particularly on soft mobile systems, and present a structured synthesis of contributions, algorithms, training strategies, and real-world applications. Unlike previous reviews that discuss soft robotics or DRL separately, this paper explicitly provides cross-comparison across DRL paradigms and soft robot tasks, enabling researchers to identify suitable DRL approaches for different soft mobile robotic behaviors. Finally, major challenges and promising future directions are proposed to advance this interdisciplinary research area.