강화학습, 물리엔진 (RaiSim), 보행로봇 연구

The goal of our research is to develop dynamic, versatile and energy-efficient control methods for legged robots. Realizing such characteristics on legged robots, especially from a control perspective, still remains challenging. Recently, deep reinforcement learning approaches have shed light on this problem. They have demonstrated training of performant controllers for complex mobile robotic systems and promised to solve a number of important decision-making problems in robotics in a scalable manner. We will exploit these recent advances in AI technologies to achieve unprecedented robustness and agility for legged robots.