Deep Reinforcement Learning-Based Intelligent Control Algorithm for Dual-Arm Robots

Abstract

This paper presents a review-oriented comparative analysis of deep reinforcement learning (DRL) for intelligent control of dual-arm robots. Instead of focusing on a single control algorithm, it organizes recent studies into an algorithm-task-metric framework and extracts quantitative evidence from representative applications including cooperative grasping, assembly, transportation, obstacle-aware planning, contact-rich control, and sim-to-real transfer. PPO, MAPPO, MADDPG, and SAC are compared in terms of success rate, convergence behavior, trajectory smoothness, force regulation, safety constraints, and transferability. Key design factors and future trends, including reward design, multimodal perception, safe reinforcement learning, sample efficiency, and real-robot deployment, are summarized to provide practical guidance for dual-arm intelligent cooperative control.