Learning from Natural Language Instructions in Fuzzy Coach-Player Systems

Today, the field of robotics research has been merged with several other areas and robotics applications are found in a variety of domains. Human-friendly robotics is one such relatively new and important development. Since the human-friendly robots are meant to be operated with close interactions with humans, their control interfaces should be as natural as possible and should be easy to use. Since the most convenient mode of communication for human is the natural language, human-robot interfaces of human-friendly robots should retain natural language capabilities. By observing natural language robotic systems implemented so far, we can identify an inherent drawback. Although it is convenient to issue natural language instructions, when performing a non-trivial task, it may be difficult to control a natural language controlled robot because the number of commands required to complete the task may be large. Moreover, it may be required to issue similar commands frequently, reducing the efficiency of the system. This is a result of the limited information content of a natural language instruction. This disadvantage can be eliminated by incorporating learning in the natural language interface. When implementing a learning mechanism for a natural language controlled robot, the most important consideration is the nature of human observations and the nature of human decision making. Inherently, human observations are imprecise and human decisions are subjective. Therefore, natural language controlled robots should be capable of learning from imprecise information. In this thesis, first of all a new architecture called “Fuzzy Coach-Player Architecture” is proposed for learning from natural language instructions issued by a human user. This architecture has been developed by observing the real-world relationship between a coach and a player. Then the said architecture is applied to learn three important things in robot control: i.e., situations, actions, and objects. Learning a situation is to learn what to do in certain circumstances. The theoretical basis for situation learning is developed and it is demonstrated with two path learning applications. In these applications, a robot learns certain paths by accepting verbal instructions. In the first application, a modified version of the conventional probabilistic neural network architecture is used. For the other application, a new kind of probabilistic neural network architecture is developed. Learning an action is, learning to perform a certain action asked by the user. This is demonstrated with a manipulator posture control application. In this application, a user can instruct a robotic manipulator to change its posture with complex verbal commands. Learning an object is, learning to identify an object when it is referred to by a natural language reference. In this application, instead of learning an object as it is, certain object features are learned. The advantage is, once an object feature is learned, that knowledge is applicable when identifying any other object having the same feature. By comparing experimental results with other natural language controlled robotic systems, it can be concluded that using the methods proposed in this thesis, the efficiency of natural language controlled robots can be improved.