A Survey on Dialogue Management in Human-Robot Interaction

Social robots are robots that are designed specifically to interact with their human users [14] for example by using spoken dialogue. For social robots, the interaction with humans plays a crucial role [7, 27], for example in the context of elderly care [15] or education [9]. Robots that use speech as a main mode of interaction do not only need to understand the user's utterances, but also need to select appropriate responses given the context. Dialogue management (DM), according to Traum and Larsson [88], is the part of a dialogue system that performs four key functions: 1) it maintains and updates the context of the dialogue, 2) it includes the context of the utterance for interpretation of input, 3) it selects the timing and content of the next utterance, and 4) it coordinates with (non-)dialogue modules. In spoken dialogue systems, the dialogue manager receives its input from a natural language understanding (NLU) module and forwards its results to a natural language generation (NLG) module, which then generates the output (see Figure 1). In contrast to general DM, DM in human-robot interaction (HRI) has to also consider and manage the complexity added by social robots (see Figure 1). The concentric circles of the figure describe decisions that have to be made when designing a dialogue manager for human-robot interaction. From each circle, one or more options can be chosen and combined with each other.