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	364647	Application of AI and Advanced robot control systems	1	3.0	3	★	Ho-Chiao Chuang	MSAI	43	0	上課地點：先鋒1F MIT教室

Course Plan：

Instructor	Ho-Chiao Chuang　Office Hours
Email	hchuang@ntut.edu.tw
Last Updated	2026-09-02 09:52:18
Course Objective	學生自備: 電腦及Python3 環境 課程大綱: 本課程將帶領同學循序漸進地了解現代機器人的模擬、控制、人工智慧以及實機系統整合。課程將以機器人模擬平台 MuJoCo 與開源雙足機器人 OpenDuck 為主要實作平台，讓同學從虛擬環境中的機器人建模與控制開始，逐步延伸至實際機器人的機構組裝、馬達控制、感測器整合以及控制策略部署，建立完整的機器人 Sim-to-Real 開發概念。 本課程強調「從模擬到實機、從理論到實作」的學習方式。透過課堂實作、小組協作以及 OpenDuck 實機操作，課程結束後，同學將具備 MuJoCo 機器人模擬、基本機器人控制、機電系統整合、AI／強化學習控制概念以及 Sim-to-Real 部署的基礎能力，並能進一步將所學知識應用於雙足機器人、四足機器人及其他智慧機器人系統的開發，將課堂中的理論知識轉化為實際解決機器人工程問題的能力。 This course guides students through the fundamentals of modern robotics simulation, control, artificial intelligence, and real-world system integration. Using the MuJoCo robotics simulation platform and the open-source bipedal robot OpenDuck as the primary hands-on platforms, students will begin with robot modeling and control in virtual environments, then progressively advance to physical robot assembly, motor control, sensor integration, and controller deployment. The course provides a complete introduction to the Sim-to-Real robotics development workflow. The course emphasizes learning from simulation to real robots, and from theory to practice. Through hands-on exercises, team collaboration, and direct operation of OpenDuck hardware, students will develop foundational skills in MuJoCo-based robot simulation, basic robot control, mechatronic system integration, AI and reinforcement-learning control concepts, and Sim-to-Real deployment. By the end of the course, students will be able to apply these skills to the development of bipedal robots, quadrupedal robots, and other intelligent robotic systems—turning classroom theory into practical solutions for real-world robotics engineering problems.
Course Schedule (Week 1-16)	1 Lecture Intro 2 What is Mujoco 3 What is Mujoco 4 Dynamics and Control 5 Dynamics and Control 6 Locomotion Intro 7 Locomotion Intro 8 Mid term presentation 9 Open duck assemble 10 Open duck assemble 11 RL intro 12 RL intro 13 Train and deploy 14 Train and deploy 15 Train and deploy 16 Train and deploy 17 Train and deploy 18 Final presentation
Flexible Learning (Week 17-18)	Category ● 學生分組實作及討論(Student group practice and discussion) Content Train and deploy/Final presentation Hours 3 Learning Outcomes Final presentation Assessment Percentage 35%
Evaluation and grading policy	出席率：10% 期中報告：35% 期末展示：35% 課堂表現：20%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 本課程使用 自編講義 教材 [1] Underactuated Robotics https://underactuated.csail.mit.edu/ [2] Open Duck tutorial https://github.com/apirrone/Open_Duck_Mini.git
The access to curricular consultation	每週二上午9:00-12:00的 Office Hour 先國際研發大樓1樓會議室可諮詢與課程相關的內容。
Expanding teaching and resources	● 無 (None)
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	● 無（None）
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