A Review of Robot Adaptive Control Driven by Deep Reinforcement Learning

Abstract

In complex settings like smart manufacturing and human-robot teamwork, robots face internal disturbances and external uncertainties. Traditional control methods depend on accurate models and manual parameter tuning, leading to complex adjustment, weak dist urbance rejection, and poor generalization. Deep reinforcement learning (DRL) combines deep learning's feature extraction with reinforcement learning's sequential decision-making. Through end-to-end learning, it removes the need for exact system models and has become a key approach for robot adaptive control. This paper systematically reviews DRL-driven robot adaptive control. It first outlines core concepts and theory, building the technical framework that brings together DRL and adaptive control. It then analyzes mainstream DRL algorithm improvements and hybrid methods for adaptive control, explores main issues in Sim-to-Real transfer, and discusses safe DRL control modeling under constraints. The paper also introduces typical robot applications, examines current challenges and bottlenecks, and points out future trends. This review aims to clarify the development path of DRL-driven robot adaptive control, offering reference for theory, algorithm design, and engineering practice. It is hoped that this survey will help new researchers quickly grasp the landscape of the field.