Reinforcement Learning in Dynamic Control and Robotic Autonomy
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 47
Special Issue Editors
Interests: controls and robotics; distributed optimization and learning; nonlinear systems and control theory; control applications
Special Issue Information
Dear Colleagues,
This Special Issue advances reinforcement learning (RL) for autonomous control of dynamical systems and multi-domain robotics (e.g., UAVs, UGVs, UUVs). It bridges RL with control theory to achieve stability-certified autonomy, robust decision-making, and real-time adaptability in complex, uncertain environments. We invite contributions on RL-driven solutions for the following issues:
1. Robotics autonomy:
- Motion planning, navigation, and task coordination for UAVs, UGVs, and UUVs;
- Multi-agent RL for collaborative missions (e.g., swarm robotics).
- Dynamic system control:
- Stability-gurantanteed RL for the nonlinear/adaptive control of robotic systems;
- Real-time RL in safety-critical applications (e.g., agile UAV maneuvers).
2. Perception–action integration:
- RL with sensor fusion (LiDAR, vision, IMU et al.) for robust perception;
- End-to-end RL for environment-aware control.
Topics of interest include, but are not limtted to, the following:
- RL for robotic motion planning and trajectory optimization;
- Adaptive RL control under model uncertainties;
- Distributed RL for multi-robot task allocation;
- RL-based navigation in dynamic environments;
- Sensor-fused RL for state estimation;
- Stability/robustness guarantees in RL-control systems;
- Transfer learning for cross-domain deployment;
- Real-time RL on embedded platforms.
Prof. Dr. Jing Wang
Dr. Puze Liu
Guest Editors
Manuscript Submission Information
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Keywords
- reinforcement learning
- robotic autonomy
- motion planning
- sensor fusion
- dynamic control
- stability
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