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

Survey of Deep Reinforcement Learning for Marine Robotics Control

This paper presents a survey of deep reinforcement learning (DRL) strategies applied to the control of autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs). Autonomous marine systems are increasingly deployed for tasks ranging from environmental monitoring to underwater inspection. DRL offers a promising alternative, or complement, to traditional control strategies, as it does not require a model to learn control policies. This review categorizes 49 recent works based on application, such as navigation, collision avoidance, and manipulation, and further classifies them according to algorithm type, including on-policy, off-policy, and hybrid approaches. This paper highlights key benefits, open challenges, and research gaps in applying DRL to marine robotics, aiming to advance the reliability and real-world deployment of DRL-based marine robotics.

Motion Control Technology for Quadruped Robots Based on Reinforcement Learning

Quadruped robots possess outstanding terrain adaptability and boast extensive application prospects in scenarios such as search and rescue, field exploration, and more. Nevertheless, conventional model-based motion control methods suffer from cumbersome modeling processes and poor generalization performance, making them ill-suited for unstructured complex environments. To address these limitations, this paper presents a comprehensive review of deep reinforcement learning-based motion control technologies for quadruped robots. It first organizes the fundamental theories concerning robot kinematics and reinforcement learning, then categorizes and summarizes research advances across three core research branches: gait generation, autonomous navigation, and adaptive gait transition. Furthermore, this paper analyzes prevailing challenges and corresponding countermeasures regarding hardware deployment, sample efficiency, and model generalization capacity. It points out that further integration of multi-algorithms, optimization of sim-to-real transformation and overall strategy design will be the main trends in this field. By identifying current technical bottlenecks and forecasting future development trends, this work offers valuable references for practical technical implementation and subsequent research within this field.

Reinforcement Learning Based Optimal Control: A Survey of Adaptive Dynamic Programming for Manipulators and Wheeled Mobile Robots

Deep reinforcement learning–based safe path planning for leader–follower robots

Multi-Agent Reinforcement Learning for Cooperative Manipulation in Industrial Robotics: A Systematic Review of Trends, Gaps and Research Drivers

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising paradigm to enhance coordination and adaptability in industrial settings. MARL enables multiple agents to learn and interact in shared environments to achieve common goals within complex and dynamic industrial processes. In this paper, a deep analysis of MARL applied to industrial multi-robot systems based on a systematic review is presented, with particular focus on cooperative manipulation tasks. Following PRISMA guidelines, we analyze a total of 30 articles published between 2016 and 2026, selected independently by two of the authors from an initial pool of 102 records retrieved from Scopus and Web of Science. These articles were used to address five key questions regarding MARL algorithms, control architectures, industrial applications and validation practices. These research questions seek to examine gaps and trends at the research level which are important for the development of multi-agent control technologies. This review shows a clear prevalence of model-free algorithms under Centralized Training with Decentralized Execution (CTDE) architectures, with validation mainly performed in simulation. Despite promising results and high potential for impact, critical gaps remain in scalability, reproducibility, and sim-to-real transfer, limiting real deployment in manufacturing environments. To address these challenges and fill current gaps, we outline actionable research directions, such as hybrid MARL approaches, standardized industrial benchmarks, digital twin pipelines, and safety-aware deployment strategies, to accelerate MARL adoption in industrial environments.

Hybrid Reinforcement Learning for Efficient Training of a Mobile Robot

Sample effective and stable training remains a key challenge in reinforcement learning (RL), especially for real-world applications such as mobile robot control where data collection is time-consuming and failures may be hazardous.Building on the residual reinforcement learning paradigm, this work presents, to the best of our knowledge, one of the first detailed physical studies of a residual Soft Actor-Critic (SAC) controller for camera-based lane following on a mobile robot. We combine an established stable, but sub-optimal lateral P-controller with a regularized SAC agent in a hybrid architecture. The classical controller provides baseline stability and rapid initial learning, while the RL agent learns residual corrections to improve performance. We employ a PID-inspired reward function and quadratic policy output regularization to ensure smooth control actions and effective sim-to-real transfer.The hybrid controller design enables rapid training convergence, requiring only a few epochs and outperforming the pure RL approach by two orders of magnitude in sample efficiency. This enables efficient hyperparameter tuning in simulation and opens the door to future learning directly on physical robots. Fine-tuning with only a few dozen real-world laps achieved robust transfer to the physical robot, maintaining the same architecture and hyperparameters. The method generalized effectively to new scenarios, such as lane changes.