Adaptive Deep Reinforcement Learning for Robotic Manipulation in Dynamic Environments

Abstract

Robotic manipulation tasks in dynamic and unstructured environments present significant challenges for traditional control procedures. Deep reinforcement learning (DRL) has appeared as a powerful technique to enable robots to learn complex manipulation tasks. However, the application of DRL in rapidly changing environments introduces additional complexities, such as ensuring adaptability and robustness in the face of shifting conditions. The framework combines DRL with adaptive learning strategies, allowing robots to continuously adjust their policies based on real-time environmental & response changes. The approach leverages advanced state and reward adaptation techniques and model ensembles to enhance robustness. Through extensive experimentation, the efficacy of the adaptive DRL method is established in achieving higher performance and generalization in various manipulation tasks. These results highlight the potential of adaptive DRL to enable robots to perform manipulation tasks more reliably and efficiently, tiling the technique for ampler flexible & capable robotic systems.