OUR Research Groups

Background & Objective:
Quadruped robots play a crucial role in search, rescue, and military applications, offering enhanced mobility across diverse terrains. This project aims to replicate Dogs motion data on slippery surfaces using the Laikago robot. By leveraging DeepLabCut for motion tracking and reinforcement learning for adaptive locomotion, we seek to enhance the robot's stability and performance in challenging environments.

Intertwined string actuator Group (ISA)

Objective:
Developing a novel actuator leveraging twisting and coiling mechanisms to achieve efficient weight lifting. This actuator is designed for minimal energy consumption, lightweight construction, and enhanced performance, reducing the overall system weight while maintaining high efficiency.

RBE Group

At RBE Group, we design and manufacture advanced soft electromagnetic actuators that generate linear displacement with low power consumption. Featuring a central coil, dual side permanent magnets, and a silicone rubber enclosure, our actuators are engineered for efficiency, reliability, and versatility across various applications.

Soft Hand Electro Magnet Pneumatic Valve Group (SHEMPV)

Objective:
We aim to develop a soft hand actuated using an electro-permanent magnet valve, addressing limitations in current valve designs such as bulkiness and high energy consumption.

Glucose Level Measurement Group (GLM)

The objective of this project is to develop a non-invasive method for measuring blood sugar levels in people with diabetes by using saliva as an alternative to traditional blood sampling techniques. This approach aims to make blood sugar monitoring more accessible, comfortable, and less stressful for individuals with diabetes, while ensuring accuracy and reliability in tracking glucose levels.

Lumbar Puncture Group (LP)

Objective: This paper aims to develop a robotic system for lumbar puncture that integrates force measurement and machine learning-based tissue classification. The system utilizes a load cell to measure needle insertion force and an SVM model to classify tissue layers (skin, fat, muscle, and ligament) based on force-displacement data. The goal is to enhance precision, reduce complications, and improve the safety and effectiveness of the procedure.