Course No.
262-F00008-01
Course Type
Part-time
Start Date
22 Aug 2026 (Sat)
Total Hours
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Course Detail
New
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Time
09:30am - 05:00pm -
Venue
Hong Kong Centre for Logistics Robotics, Hong Kong Science Park -
Instructor
Dr. Sui Congying - Postdoctoral Fellow at the Hong Kong Centre for Logistics Robotics (CUHK). -
Level
General Courses -
Tuition Fee
HK$3,800 -
Language Used
English -
Closing Date for Application
08 Aug 2026 -
Course Enquiries
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Workshop Introduction
The workshop is organized in collaboration with the Hong Kong Centre for Logistics Robotics (https://hkclr.hk/). It is designed for high school and junior college students, as well as other beginners who are interested in robotics and AI. As participants, you will learn the complete workflow of embodied AI: controlling a real robotic arm, collecting and labelling robot behaviour data (success/failure), finetuning your first robotic AI model, and testing your model on the physical robot. Through guided activities and live demonstrations, every participant will experience how a robot can learn from human examples.
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Workshop Rundown
| Session | Content |
|---|---|
| A.M. (09:30 - 12:30) | ．Assembly point: Science Park ．Scope of training: Robotic arm basics, VR-based data collection for robot learning, visual interpretation of the training process |
| Noon (12:30 - 14:00) | ．Free time for lunch |
| P.M. (14:00 - 17:00) | ．Scope of training: Evaluation of model capability, comparison of model test results, introduction to principles of Vision Language Action (VLA) ．First hand insights into robotics/AI research and further study pathways |
Learning Outcomes
Upon successful completion of this course, participants will be able to:
1. Manually control a robotic arm – using drag‑and‑drop, teleoperation, and motion sequence recording.
2. Collect and process grasping data, and launch a model training pipeline.
3. Train their own AI model and apply it to make the real robot grasp an object.
1. Manually control a robotic arm – using drag‑and‑drop, teleoperation, and motion sequence recording.
2. Collect and process grasping data, and launch a model training pipeline.
3. Train their own AI model and apply it to make the real robot grasp an object.
Remarks
No prior coding or robotics experience is needed, but students are required to bring their laptop to the workshop