This thesis studies mechanisms to improve human-robot-interaction through a spoken dialogue for household robots. Therefore, a full dialogue system, in which the semantics of the words play an important role, is implemented. Nowadays, robots are found to be helpful in a lot of applications. One important eld during the last decades is to design household robots capable of helping people with disabilities. For this purpose, the robot has to be able to communicate with humans so it knows what it has to do. A natural way to do so is by speech, which still needs further research. With the controller implemented, the robot is expected to grasp objects, navigate, learn and follow people, clean up a room, etc. So the rst goal of this thesis is to give the robot the ability to understand these tasks through a spoken dialogue system. Furthermore, the robot has to be able to understand complex tasks where the information to achieve the task is incomplete. The second goal of this thesis is that the robot has the ability to learn new words, for example, names of people, because it is impossible to have all the names stored in a database in advance. In addition, learning people not only implies learning the physical body, e.g., the face, but also their names. The dialogue system implemented has an accuracy of 80.45% for isolate words, 90.56% for simple commands and 96.66% for complex commands that provide incomplete information for the robot to achieve the task. Finally, the accuracy of recognizing words learnt on-line is 33.33%.