Hierarchical bayesian model improves robot group talk and clarity
HiBRIDGE: A Hierarchical Bayesian Neural Network Framework for Interpretable Dialogue Management in Group-Robot Interaction
Robotics
Summary
In conversations with groups, a robot must decide who to talk to and what to say, which can be tricky when multiple choices seem right. The authors created HiBRIDGE, a system that uses a step-by-step approach and probability to handle uncertainty and learn from little data. This helps the robot make decisions that are easier to understand and explain to humans. Tests show their method outperforms others and that people find its explanations clearer and more useful during real interactions.
What this means in practice
- •For robotics developers: Build socially aware robots that decide how to join group conversations with clearer explanations of their choices.
- •For customer service automation teams: Enhance multi-agent chatbots to manage group dialogues better by selecting whom to address and providing interpretable responses.
Authors
Massimiliano Nigro, Hatice Gunes, Micol Spitale, Fethiye Irmak Dogan
Abstract
In multi-party human-robot interaction, a robot must continuously decide whom to address and what to say to participate effectively in the conversation. In real-world interactions, this is challenging because several behaviours may be plausible at the same time: a robot might continue a topic with one participant, involve another through a question, or address the whole group, with the appropriate choice depending on both whom it addresses and the interaction context. Current approaches remain limited in representing uncertainty when several behaviours are plausible and in structuring decisions into semantically meaningful intermediate steps that make robot decisions easier to interpret. Addressing these, we present HiBRIDGE, a hierarchical Bayesian neural network framework for group-robot dialogue management. Its Bayesian formulation enables uncertainty-aware prediction and robust learning from limited interaction data, while the hierarchical approach formulates behaviour selection as a structured, multi-stage decision process. We further use decision-tree surrogates to investigate whether this structure can support more interpretable explanations. Across three offline group-HRI datasets, our findings show that Bayesian formulations outperform their deterministic counterparts and several state-of-the-art baselines. Next, through an online study (N=20), we show that explanations derived from the hierarchical model are rated as more helpful for understanding robot behaviour and are preferred over those derived from the flat model. Finally, through our in-person study (N=12), we demonstrate the feasibility of HiBRIDGE for autonomous real-time group interaction, with both hierarchical and flat Bayesian variants positively perceived. Overall, HiBRIDGE combines strong predictive performance with a structured decision process that supports more interpretable explanations of robot behaviour.