Robots That Learn Language: A Developmental Approach to Situated Human-Robot Conversations

Recent progress in sensor technologies and in an infrastructure for ubiquitous computing has enabled robots to sense physical environments as well as the behaviour of users. In the near future, robots that change their behaviour in response to the situation in order to support human activities in everyday life will be increasingly common, so they should feature personally situated multimodal interfaces. One of the essential features of such interfaces is the ability of the robot to share experiences with the user in the physical world. This ability should be considered in terms of spoken language communication, which is one of the most natural interfaces. The process of human communication is based on certain beliefs shared by those communicating (Sperber & Wilson, 1995). Language is one such shared belief and is used to convey meaning based on its relevance to other shared beliefs. These shared beliefs are formed through interaction with the environment and other people, and the meaning of utterances is embedded in such shared experiences. From this viewpoint, spoken language interfaces are important not only because they enable handsfree interaction but also because of the nature of language, which inherently conveys meaning based on shared experiences. For people to take advantage of such interfaces, language processing methods must make it possible to reflect shared experiences. However, existing language processing methods, which are characterized by fixed linguistic knowledge, do not make this possible (Allen et al., 2001). In these methods, information is represented and processed by symbols whose meaning has been predefined by the machines' developers. In most cases, the meaning of each symbol is defined by its relationship to other symbols and is not connected to perception or to the physical world. The precise nature of experiences shared by a user and a machine, however, depends on the situation. Because it is impossible to prepare symbols for all possible situations in advance, machines cannot appropriately express and interpret experiences in dynamically changing situations. As a result, users and machines fail to interact in a way that accurately reflects shared experiences. To overcome this problem and achieve natural linguistic communication between humans and machines, we should use methods that satisfy the following requirements.