Goal:
The course aims to deliver the principles of Autonomous Intelligence and to equip students with the theoretical foundations and practical skills required to design, analyze, and implement autonomous intelligent systems embedded in physical environments.
Course description: This course equips students with a comprehensive understanding of Autonomous Intelligence, focusing on the principles and system architectures that enable intelligent behavior to emerge through continuous interaction between perception, cognition, action, and the physical world. The course explores embodied and embedded intelligent systems operating in real-world environments, emphasizing physical platforms such as robots, manipulators, and autonomous embedded systems rather than purely software-based agents.
Students will gain hands-on experience in designing and implementing autonomous intelligent systems by integrating learning, reasoning, and control, using modern AI techniques including large language models, reinforcement learning, multimodal perception, and sensorimotor learning. The course also introduces systems and parallel intelligence approaches for deploying autonomous intelligence in complex, large-scale, and dynamic environments. Emphasis is placed on building closed-loop, adaptive, and goal-directed autonomous systems capable of robust operation under physical constraints, uncertainty, and real-world interaction, with critical analysis of applications such as robotics, autonomous manipulation, and human–machine collaboration.
Autonomous Intelligence in Practice — An Interdisciplinary and Systems Intelligence Approach