How to Make a Robot Grumpy Teaching Social Robots to Stay in Character with Mood Steering

Conveying a robot's target mood is crucial to successful social interactions. The robot's expressive performance must be appropriate, persuasive, and consistent. However, this is challenging when interactions contain a mixture of scripted and improvised content, such as those generated by language models. In this paper, we take on the task of teaching robots to stay in character, that is to say, exhibit consistency in mood during interactions. We start by defining a communication strategy module that allows for the top-down specification of a target robot mood for a given task, goal, or context. We then propose a mood steering framework for enforcing robot mood consistency throughout an interaction that supports several target moods. Our framework consists of two components: 1. expressivity steering specifies the speech and behavior to be used by the robot to convey a target mood, and 2. language model steering ensures that improvised language is consistent with the robot's target mood. As a first step toward identifying effective communication strategies, we implement grumpy and cheerful strategies for a collaborative storytelling game and compare them to a neutral baseline. Evaluation in a collaborative storytelling game shows that our approach generates robot behavior that successfully conveys the robot's target mood throughout gameplay and language model steering generates story contributions that capture the target mood without quality degradation and raises important issues for communication strategy design.