Manipulation is one of the most classical problems in robotics. Recently, the field has experienced a considerable upward momentum with tremendous interest from industry (e.g. manufacturing, logistics). This is partly due to the recent progress in computer vision and deep learning which have enabled new application scenarios. Existing applications are mostly about pick and place. Our lab is interested in more challenging dynamic manipulation problems where dynamics, motion planning, and control play a very important role. Existing software tools such as OpenRAVE, MoveIt!, and OMPL mostly rely on sampling based methods, which are not sufficient for dynamic manipulation problems. Our goal is to develop new algorithms and software packages to enable fast online planning and tracking control for dynamic manipulation tasks in unstructured and cluttered environment.