This project focuses on the theme of “Application and Development of AI-Based Home Service Robots.” The primary objective is to develop intelligent service robots capable of performing highly autonomous tasks in everyday home environments. These robots will be applied in scenarios such as long-term care companionship, smart home management, and medical assistance, with the goal of improving the efficiency of home care and overall quality of life. 

The expected outcomes include: a) establishing an integrated AI robotic framework encompassing perception, language understanding, and action execution; b) cultivating interdisciplinary talent; c) promoting international research collaboration; and d) enhancing the technological capabilities of service robotics. 

Duration of the Internship: 4-5 months, basically (dependent upon the student's availability and capability, minimum 3 and maximum 6 months). The internship is divided into 3 different groups:

Robotics: 
a.  Our goal is to develop a small vision-language-action (VLA) model to help a robotic arm break down complex instructions into smaller, logical steps. Our goal is to add better task planning without making the model too heavy for edge devices.

b.  Developing simple world models so the system can predict what will happen in its environment. This lets the model plan ahead internally while still running smoothly on low-power hardware  

Agriculture: 
The current general approach in self-learning, where we minimise the human manual effort needed (manual labelling, etc.)
What we aim to do is to find a new self-learning method outside of the general student-teacher method.
UAV:
The current focus for the drone is implementing obstacle avoidance and Vision-Language-Action (VLA) integration on an autonomous drone system.
What we want to aim for is to simulate and implement obstacle avoidance that is sustainable for the drone in the unknown environment.

Project Outcomes: Completion of research reports will be submitted to international conferences or academic journals. 

Preferred Intern Educational Level: i) Master's graduate students are preferred and first considered, next are doctoral graduates, and then the senior undergraduates. Be noted that junior undergraduates and younger individuals won’t be considered. ii) Major in CS, EE, or Mechanical Engineering.