This paper presents the integrated service robot control platform developed at the Cognitive Technology Research Center in Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. The platform is equiped with a sensor named Kinect. We can extract 2D and 3D features from the Kinect’s color images, depth images and point clouds, then classify the objects’ geometric models and choose the right grab attitude for the robot’s hand after those two types of features have been fused. Following the theory of Learning from demonstration (LfD), we have developed an approach to improve robot’s cognitive learning ability for indoor object manipulation tasks in everyday household environments, such as identifying the door handle’s location and opening the door, recognizing the targets in the cupboard, grabbing the object and sending it to the designated location, etc. Finally, we design an experiment to prove our method, which can teach the robot to grasp some cylindrical, rectangular or other geometrical objects and then finish the complicated task of planning the motion trajectory while interacting with the surrounding environment.