I am interested in robotics and intelligent control system. The goal is to develop a intelligent robotic system such that a robot is able to learn how to perform a task in a fast and safe fashion. This is a new exciting field where AI meets well-established traditional control theory.
I am currently working on my master thesis: "Autonomous Blimp Navigation using Model-Based Reinforcement Learning". The goal is to develop a safe learning control algorithm and implement it on a blimp so that the blimp learns how to navigate itself in any environment safely. The idea is to use recent uncertainty-aware deep neural network to model the complex dynamics of a blimp. In learning phase, to avoid taking dangerous actions, we use model-uncertainty as an indicator to switch the pilot between neural network controller and classic PID controller to stabilize the blimp. Once the dynamic model is learned, the algorithm can then harness it for safe motion planning in order to achieve optimal performance.
I am a master student in Max Planck Institute Intelligent System and Automation and IT, Technical University of Cologn. Before that, I have done my bachelors in Electrical Engineering from National Tsing Hua University, Taiwan.
Autonomous MoCap systems, like AirCap, rely on robots with on-board cameras that can localize and navigate autonomously. More importantly, these robots must detect, track and follow the subject (human or animal) in real time. Thus, a key component of such a system is motion planning and control of multiple...
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems