In this video, we consider the task of a personal robot organizing a room by placing objects stably as well as in semantically preferred locations. While this includes many sub-tasks such as grasping the objects, moving to a placing position, localizing itself and placing the object in a proper location and orientation, it is the last problem-how and where to place objects-is our focus in this work and has not been widely studied yet. We formulate the placing task as a learning problem. By computing appearance and shape features from the input (point clouds) that can capture stability and semantics, our algorithm can identify good placements for multiple objects. In this video, we put together the placing algorithm with other sub-tasks to enable a robot organizing a room in several scenarios, such as loading a bookshelf, a fridge, a waste bin and blackboard with various objects.