1. Explain the definition, scope, and significance of Physical AI, and distinguish it from purely digital and agentic AI systems.
2. Describe the core concepts, system architectures, and key enabling technologies underlying Physical AI, including sensing, perception, reasoning, planning, and control.
3. Analyze and apply foundational Physical AI techniques to real-world problem settings, taking into account environmental dynamics, embodiment, and system constraints.
4. Identify and evaluate major application domains and representative use cases of Physical AI, such as robotics, smart environments, and autonomous systems.
5. Design, implement, and prototype a Physical AI solution that demonstrates the integrated use of perception, decision-making, and physical action.
Note: The lectures during the week of the 12th–14th will be co-taught with Dr. Chun-yien
Chang, a research scientist in the Department of Computer Science at National Yang Ming Chiao Tung University. Dr. Chang will cover ontology engineering and ontology-based evaluation.

1. Explain the definition, scope, and significance of Physical AI, and distinguish it from purely digital and agentic AI systems.
2. Describe the core concepts, system architectures, and key enabling technologies underlying Physical AI, including sensing, perception, reasoning, planning, and control.
3. Analyze and apply foundational Physical AI techniques to real-world problem settings, taking into account environmental dynamics, embodiment, and system constraints.
4. Identify and evaluate major application domains and representative use cases of Physical AI, such as robotics, smart environments, and autonomous systems.
5. Design, implement, and prototype a Physical AI solution that demonstrates the integrated use of perception, decision-making, and physical action.
Note: The lectures during the week of the 12th–14th will be co-taught with Dr. Chun-yien
Chang, a research scientist in the Department of Computer Science at National Yang Ming Chiao Tung University. Dr. Chang will cover ontology engineering and ontology-based evaluation.

自編教材

Self-edited teaching materials