Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	367087	Physical AI	1	3.0	3	★	Jyh-Cheng Jeng	遠距教學班(研究所)	0	0	陽交陳奕廷https://ppt.cc/fCH4qx

Course Plan：

Instructor	Jyh-Cheng Jeng　Office Hours
Email	jcjeng@ntut.edu.tw
Last Updated	2026-08-07 15:22:57
Course Objective	Physical AI refers to AI systems that can perceive, reason, and act within real-world environments—sensing through cameras, microphones, and other sensors, and interacting with the world via motors, robotic arms, wheels, or other actuators. In this course, Physical AI serves as the central lens through which all projects and discussions are contextualized. Students will explore how modern AI models are integrated into real-world systems such as robots, smart homes, and autonomous vehicles, and how intelligence, embodiment, and environment jointly shape system behavior. Intended Learning Outcomes: 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.
Course Schedule (Week 1-16)	週次 日期 課程內容 備註 1 9/8/2026 Introduction 2 9/15/2026 Recent Advances in Physical AI 3 9/22/2026 Data Pyramid in Physical AI 4 9/29/2026 Introduction to Universal Manipulation Interface (UMI) and LeRobot 5 10/6/2026 Simultaneous Localization and Mapping HW1 Release 6 10/13/2026 Path Planning 7 10/20/2026 Robotic Manipulation HW2 Release 8 10/27/2026 Introduction ICRA'26 WBCD Challenge Link: https://wbcdcompetition.github.io/ 9 11/3/2026 Introduction to Issac Sim HW3 Release and course project spec release 10 11/10/2026 Imitation Learning/Reinforcement Learning for Robotics Manipulation 11 11/17/2026 Diffusion Models in Robotics Manipulation HW4 Release 12 11/24/2026 Introduction to Ontology Engineering 13 12/1/2026 Ontology-based Evaluation HW5 Release 14 12/8/2026 Scenario-based Safety Validation and Policy Evaluation 15 12/15/2026 Research Frontiers 16 12/22/2026 Course Project Presentation
Flexible Learning (Week 17-18)	Category ● 遠距教學(Distance learning) Content 17: Course Project Presentation (Continued) 18: Course Experience Sharing Hours 6 Learning Outcomes Course Project Presentation Course Experience Sharing Assessment Percentage Course Project Presentation (Continued): 5% Course Experience Sharing: 5%
Evaluation and grading policy	Computer Assignment: 50% - HW1: Coordinate Transformation - HW2: 3D Scene Reconstruction and Mapping - HW3: Path Planning - HW4: Robotics Manipulation - HW5: Ontology-based Data Quality Verification Course Project : 40% The Course Project is designed to provide students with hands-on experience across the full lifecycle of Physical AI—from real-world data collection, to policy learning, and ultimately to evaluation.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 參考書目： 1. Stuart Russell and Peter Norvig (2020). Artificial Intelligence: A Modern Approach, 4th Global ed., Pearson College. 2. Richard Hartley and Andrew Zisserman (2003), Multiple View Geometry in Computer Vision. 3. Timothy D. Barfoot (2024), State Estimation for Robotics, Second Edition, Cambridge University Press. 4. Kevin Lynch and Frank Park (2017), Modern Robotics Mechanics, Planning, and Control, Cambridge University Press. 5. Keet, C. M. (2025). An introduction toontology engineering (2nd ed.). College Publications. 6. Allemang, D. (2020). Semantic Web for the Working Ontologist : Effective Modeling for Linked Data, RDFS, and OWL / (Third Edition). Association for Computing Machinery. https://doi.org/10.1145/3382097
The access to curricular consultation	Line Group of the Course Email: jcjeng@ntut.edu.tw
Expanding teaching and resources	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
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