Beijing University of Chemical Technology
Syllabus for Embodied Intelligence Practice _
Ⅰ. General Information
Course Code |
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Course Information | Academic Discipline | Robotics Engineering | Knowledge Domain | 7 Robot Sensing | |||||
Total Class Hours | 3 weeks | Credits | 2 | Lecture Hours | 0 | Laboratory Hours | 0 | Computer Lab Hours | 0 |
Course Title (in Chinese) | 具身智能实践 | ||||||||
Course Title (in English) | Embodied Intelligence Practice | ||||||||
Applicable Majors | Robotics Engineering | ||||||||
Semester Available | The seventh semester | ||||||||
Prerequisites (Course Title) | Fundamentals and Applications of Artificial Intelligence, Robot Localization and Navigation | ||||||||
Corequisites (Course Title) | Robot operating system | ||||||||
Brief Course Description | Embodied Intelligence Practice is a compulsory practical course in the field of robotics engineering. Through embodied intelligence practice, one can further understand and master the design and application methods of fully autonomous robots. Through practical design, exposure to actual processing and control processes, consolidate and deepen understanding of theoretical knowledge learned in the classroom, including design methods involved in the design process of embodied intelligent robots such as computing, perception, and positioning systems, planning and control systems, and the application of large models, etc., to prepare for becoming a robot engineering and technical talent. | ||||||||
Ⅱ.Curriculum Nature and Course Objectives
Embodied Intelligence Practice is a compulsory practical course for robotics engineering majors in higher education institutions. Through the teaching of this course, students can achieve the following goals:
G1: Enable students to understand and master the fundamentals of computation, perception, positioning, planning, and control of embodied intelligent robots through practical experience.
G2: Enable students to understand and master optimization methods for embodied intelligent robot systems in practice, including algorithm safety and system reliability.
G3: Enable students to understand and master the comprehensive design method of embodied intelligent robots in practice.
Ⅲ. The Corresponding Relationship between Course Objectives and Graduation Requirements
Table 1 Correspondence between course objectives and graduation requirements
Graduation requirements | Index point | Course objectives | Way to reach | Evaluation basis | Degree of support |
2.1 | 2.1 | G1 | Study during and after class | Normal study and final exam | L |
4.1 | 4.1 | G2 | Study during and after class | Normal study and final exam | H |
10.2 | 10.2 | G3 | Study during and after class | Normal study and final exam | M |
Ⅳ. Teaching Contents and Requirements for the Lecturing Part
4.1 Comprehensive design of embodied intelligent robots (2 weeks)
4.1.1 Teaching Objectives (G1, G2, G3)
4.1.2 Teaching Content
(1) Design tasks for embodied intelligent robots
Group customization of embodied intelligent robot design tasks, with clear design objectives.
(2) Comprehensive design of embodied intelligent robots
Master the basics of computing, perception, positioning, planning, and control of embodied intelligent robots, apply design methods in practice, and complete embodied intelligent robot design tasks.
4.1.3 Teaching requirements: Master the design methods of embodied intelligent robots and complete practical design tasks.
4.2 Optimization of embodied intelligent robot system (1 week)
4.2.1 Teaching Objectives (G2, G3)
4.2.2 Teaching Content
(1) Optimization of embodied intelligent robot system
Identify robot design issues in practice, apply optimization methods of embodied intelligent robot systems to optimize the design, and adjust the design scheme.
4.2.3 Teaching requirements: Understand and master the optimization methods of embodied intelligent robot systems.
Ⅴ.Teaching Contents and Requirements for the Practical Part
No
Ⅵ. Evaluation Standards
The assessment methods of this course include process assessment and final assessment. The process assessment includes class performance (including attendance, questioning, answering questions, group discussion) and class test.
The final assessment of the course is a comprehensive assessment of the process assessment and the final assessment, and based on this, the achievement of the course objectives is evaluated.
Ⅶ.Textbooks and Recommended References
7.1 Teaching materials
[1] Gan Yiming et al., Embodied Intelligent Robot System, Electronic Industry Press, First Edition, 2024
7.2 Reference books
[1] Liu Ruochen, Introduction to Artificial Intelligence, Tsinghua University Press, 2021
[2] Zhang Weinan, Shen Jian, Yu Yong et al. Hands-on Learning and Reinforcement Learning, Posts and Telecommunications Press, 2021
Written by Guohua Chen