Research

Dr. Tao's research primarily focuses on Learning-Based Robot Control, particularly Autonomous Robots and Human-Machine Systems. The aim is to enhance the learning capability and intelligence of robots to perform dexterous manipulation tasks autonomously or to adapt and cooperate with human operators naturally and intuitively. Thus far, the research applications include

1) Semi-autonomous Telemanipulation, investigating the methodology to convey the human command to the robot with intuitive motion mapping through an autoencoder, enabling the robot to provide active assistance based on the human preference, and transferring such assistive knowledge between different robots.

2) Human-Robot Cooperation (HRC): investigating the learning strategies to establish cooperation between the robot and the human, and improve the cooperation performance and task performance in HRC.

3) Learning-Based Dexterous Manipulation, improve the intelligence and capability of the autonomous robot with adaptive hierarchical curriculum and multi-agent approaches in complex tasks, such as multi-phase multi-objective manipulation and multi-finger dexterous in-hand manipulation.