My research focuses on efficient machine learning for robotics including deep reinforcement learning and computer vision. These days, I am particularly interested in neural ODE and its applications in developing scalable imitation learning algorithms, physics-informed neural networks (PINN) to learn residual dynamics and residual actions, also deep learning approaches for visual object perception, segmentation and grasp pose prediction in highly cluttered environments.