Things that Make Robots Go HMMM : Heterogeneous Multilevel Multimodal Mixing to Realise Fluent, Multiparty, Human-Robot Interaction

Fluent, multi-party, human-robot interaction calls for the mixing of deliberate conversational behaviour and re- active, semi-autonomous behaviour. In this project, we worked on a novel, state-of-the-art setup for realising such interactions. We approach this challenge from two sides. On the one hand, a dialogue manager requests deliberative behaviour and setting parameters on ongoing (semi)autonomous behaviour. On the other hand, robot control software needs to translate and mix these deliberative and bottom-up behaviours into consistent and coherent motion. The two need to collaborate to create behaviour that is fluent, naturally varied, and well-integrated. The resulting challenge is that, at the same time, this behaviour needs to conform to both high level requirements and to content and timing that are set by the dialogue manager. We tackled this challenge by designing a framework which can mix these two types of behaviour, using AsapRealizer, a Behaviour Markup Language realiser. We call this Heterogeneous Multilevel Mul- timodal Mixing (HMMM). Our framework is showcased in a scenario which revolves around a robot receptionist which is able to interact with multiple users.