This track focuses on the latest developments in artificial intelligence that enhance the capabilities of robots in interacting with humans. Papers will explore novel algorithms, machine learning techniques, and their applications in real-world scenarios.

This session invites contributions on the engineering principles and practices involved in the design and implementation of robotic systems. Topics may include structural design, materials selection, and integration of sensors and actuators.

This track emphasizes the software engineering methodologies and tools that are essential for developing autonomous robotic systems. Discussions will include software architecture, design patterns, and testing frameworks tailored for robotics applications.

This session will cover the application of machine learning techniques to improve robotic perception, decision-making, and adaptability. Contributions may include supervised, unsupervised, and reinforcement learning approaches in various robotic contexts.

This track addresses the design and implementation of control systems that govern robotic behavior. Papers will discuss classical and modern control strategies, stability analysis, and real-time control challenges.

This session focuses on the development of perception systems that enable robots to understand and interact with their environment. Topics include sensor fusion, computer vision, and environmental mapping.