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Name of Lab:Precision System Control Lab (PSCL)

Research Interest:Nano-Manipulation, Embedded Systems, Precision Servo

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Task Control of  Robotic Arm in Complex Environments Using Deep Reinforcement Learning with Actor-Critic Models



In this research, machine learning is employed for the trajectory control of a robotic arm to achieve more precise control of repetitive motions. Various learning methods are utilized to identify the optimal controller, and a comparison is made based on the end-point position and pose errors of the robotic arm to fulfill the specified task requirements.

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Novel Trifocal Artificial Crystalline Lens Design


Biocompatible Optical Transparent Material for Artificial Crystalline Lens Design: Mold Fabrication, Diamond Cutting Process, Simulation, and Experimental Optimization.


Development and Validation of Muscle Endurance-Enhanced Powered Exoskeleton System


With the increasing application of human-machine interaction, human intent detection has become increasingly important. In this research, a machine learning system is used for the intent prediction of different movements performed by the general public. Participants wear inertial measurement units on their right legs to measure and calculate the angles, angular velocities, and angular accelerations of their joints. The results of this study show promising outcomes. We also adjusted other parameters and used the same training data to predict the intent of different participants, demonstrating that the system can still perform intent prediction even when training and testing data come from different individuals.

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