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| DESCRIPTION | This course aims at guiding undergraduate students of the ROAS major for their academic path and future. This course is mostly introductory and is proposed to inspire UG students for their academic path development and growth of maturity in four years of UG study. Activities may include seminars, workshops, advising and sharing sessions, interaction with faculty and teaching staff, and discussion with student peers or alumni. Grading type: Pass/ Fail |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6514) | 01-SEP-2026 - 28-NOV-2026 We 10:30AM - 11:50AM | Rm 201, W2 | DING, Fangqiang HUANG, Qiang JI, Yiding LI, Haoang MA, Jun NIE, Qiang SONG, Jie YANG, Jun YASA, Immihan Ceren YASA, Oncay ZHAO, Hang ZHU, Lei | 40 | 38 | 2 | 0 |
| PREVIOUS CODE | ROAS 6000L |
|---|---|
| DESCRIPTION | Formal methods originate from theoretical computer science and have been seamlessly integrated with dynamical systems. At its core, formal methods involve formulating specifications to form proof obligations, verifying that the systems indeed meet their specifications via algorithmic proof search, and designing systems to meet those obligations. This course bridges fundamental gaps between formal methods and control theory. It introduces fundamental theories and techniques of formal methods that apply broadly to various dynamical systems, such as robots, autonomous systems and cyber physical systems. Particularly, the following topics will be covered: symbolic system modeling, regular and omega-regular properties, linear temporal logic, model checking, system simulation and abstraction, game theoretic control synthesis and cutting-edge engineering applications. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6243) | Mo 09:00AM - 11:50AM | Rm 202, W1 | JI, Yiding | 20 | 20 | 0 | 0 |
| EXCLUSION | BSBE 5700 |
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| CO-LIST WITH | BSBE 5700 |
| DESCRIPTION | This course introduces microscopic robots and covers the fundamentals of their design, fabrication, and control for healthcare applications. Students will explore the interdisciplinary fields of engineering, materials science, medicine, and nanotechnology, and understand how these robots are set to revolutionize diagnostics, targeted therapy, and minimally invasive surgery. This course aims to equip students with the theoretical knowledge and practical insights necessary to contribute to this rapidly advancing field. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6244) | We 01:30PM - 04:20PM | TBA | YASA, Immihan Ceren | 20 | 0 | 20 | 0 | > The classroom is E1233. |
| PREVIOUS CODE | ROAS 6000B |
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| DESCRIPTION | Light traveling in the 3D world interacts with the scene through intricate processes before being captured by a camera. These processes result in the dazzling effects like color and shading, complex surface and material appearance, different weathering, just to name a few. Physics based vision aims to invert the processes to recover the scene properties, such as shape, reflectance, light distribution, medium properties, etc., from the images by modelling and analyzing the imaging process to extract desired features or information. This course introduces the advanced methodologies in the context of physical-based vision for robotics and autonomous systems. We will introduce diverse techniques, covering from traditional methods based on hand-crafted features to recent deep learning methods. Apart from the fundamental knowledge in physical-based vision, the students will also have opportunities to discover and learn cutting-edge methodologies in popular physical-based vision topics (i.e., bad-weather restoration, shadow detection and removal, specular highlight detection and removal, intrinsic image decomposition, reflection detection and removal, and so on) of the physical-based vision, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6245) | Tu 03:00PM - 05:50PM | Rm 202, W4 | ZHU, Lei | 40 | 40 | 0 | 0 |
| ATTRIBUTES | [BLD] Blended learning |
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| EXCLUSION | INTR 5330 |
| CO-LIST WITH | INTR 5330 |
| DESCRIPTION | The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6246) | Th 09:00AM - 11:50AM | TBA | HE, Dengbo | 20 | 14 | 6 | 0 | > The classroom is E1237. > The class is delivered in a blended learning mode. |
| DESCRIPTION | This course introduces the transformative role of robotics in modern healthcare, exploring its applications across diagnostics, surgery, rehabilitation, and patient care. Students will gain a comprehensive understanding of how robotics is changing healthcare delivery, from minimally invasive surgical systems like the Da Vinci Surgical System to assistive robots that enhance daily living for individuals with disabilities. In addition, it addresses the integration of AI and machine learning in healthcare robotics, including predictive analytics and AI-assisted diagnostics. Emerging trends, including micro/nanorobots and soft robots, are also discussed to prepare students for future innovations. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6250) | We 01:30PM - 04:20PM | Rm 201, E3 | YASA, Oncay | 20 | 13 | 7 | 0 |
| DESCRIPTION | Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6251) | Mo 04:00PM - 04:50PM | Rm 101, W1 | SONG, Jie | 100 Quota/Enrol/Avail ROAS students: 100/30/70 | 130 | 0 | 0 |
| DESCRIPTION | An independent study on selected topics carried out under the supervision of a faculty member. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6247) | TBA | TBA | TBA | 50 | 1 | 49 | 0 |
| DESCRIPTION | Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6248) | TBA | No room required | TBA | 80 | 64 | 16 | 0 |
| DESCRIPTION | Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6249) | TBA | No room required | TBA | 120 | 42 | 78 | 0 |