Autonomous Multi-Agent Systems in Robotics: Perception, Decision-making, Control, Learning, and Optimization Methods

Chair:	Co-Chair:
Xin Tong  Nanjing University, China	Jie Chen University of Science and Technology of China, China

Key Words: Multi-Agent Systems, Autonomous Robots, Perception, Decision-Making, Control, Learning and Optimization

Information: This special session focuses on recent advances, challenges, and applications of autonomous multi-agent systems in robotics. It aims to bring together researchers and practitioners to discuss state-of-the-art theories, algorithms, and implementations related to multi-agent perception, cooperative decision-making, motion control, learning-based strategies, and optimization methods. It welcomes contributions covering distributed perception, collaborative planning, robust and safe control, multi-agent reinforcement learning, model predictive control, distributed optimization, task allocation, and real-world deployment in robotic systems. The session seeks to promote cross-disciplinary ideas and foster innovations for building reliable, scalable, and intelligent autonomous multi-agent systems.

Topics of interest include but are not limited to: 

• Distributed perception and environment cognition for autonomous multi-agent robotic systems
• Collaborative decision-making and task allocation in multi-robot systems
• Safe, robust and adaptive control of networked multi-agent robotic systems
• Learning-based strategies for multi-agent coordination and motion planning
• Distributed optimization and optimal control in multi-robot systems
• Integration of perception, decision-making, control and learning for autonomous multi-agent systems

Submission Deadline: April 28, 2026 (the first round)