Towards Autonomous and Trustworthy Robotic Systems: Intelligent Control, Motion Planning, and Embodied Decision-Making

Intelligent control and planning constitute the decision-making core of modern robotic systems, enabling autonomous operation, adaptive behaviors, and optimized task execution in complex, dynamic environments. As robotics advances toward higher levels of autonomy, the synergy between perception, decision-making, and motion generation has become increasingly vital. In this context, sophisticated control and planning algorithms not only enhance operational efficiency but also empower robots to safely interact and adapt within unpredictable real-world scenarios.Recent progress in model predictive control, learning-based planning, and collaborative human-robot interaction frameworks has demonstrated the pivotal role of algorithmic innovation in achieving robust performance across various applications—from autonomous navigation in cluttered environments to precise manipulation in manufacturing. Developing such systems, however, involves addressing persistent challenges including real-time computational efficiency, handling uncertainties, ensuring safety and stability, and balancing exploration with exploitation.Emerging AI-driven methodologies, such as deep reinforcement learning, neural motion planning, and sim-to-real transfer techniques, are creating new avenues for tackling these complexities and designing highly adaptive, resilient robotic systems capable of operating in diverse, unstructured settings.Modern robotic systems face a fundamental challenge: bridging the gap between perception, decision-making, and physical execution in unstructured, real-world environments. Despite remarkable progress in machine learning and advanced control theory, robots still struggle to generalize across diverse tasks, handle unexpected perturbations, and operate safely alongside humans. Traditional model-based control lacks adaptability, while purely data-driven approaches often sacrifice interpretability and formal safety guarantees.This Special Issue focuses on the latest advances in intelligent control and motion planning strategies that enable robotic systems to operate autonomously, adaptively, and safely in complex, dynamic environments. We welcome original contributions that bridge control theory, artificial intelligence, and real-world robotic applications, with emphasis on methodologies that enhance decision-making, trajectory planning, and task execution under uncertainty. Topics of interest include but are not limited to:• Model-based and data-driven control for manipulators and mobile platforms;• Learning-based motion planning, including reinforcement and imitation learning;• Multi-robot coordination, task allocation, and distributed decision-making;• Perception-aware planning and robust control under sensor and model uncertainty;• Human-robot interaction, shared autonomy, and safe control frameworks;• Whole-body control, multi-contact planning, and dynamic locomotion;• Real-time optimization and predictive control for time-critical robotic tasks;• Sim-to-real transfer and deployment in unstructured environments. Topic editors: 阳 刘, Boyu Ma, Zhengyuan Zhang Source: https://www.frontiersin.org/research-topics/83131/towards-autonomous-and-trustworthy-robotic-systems-intelligent-control-motion-planning-and-embodied-decision-making