Quadrupedal Spine Control Strategies: Exploring Correlations Between System Dynamic Responses and Human Perspectives

Unlike their biological cousins, the majority of existing quadrupedal robots are constructed with rigid chassis. This results in motion that is either beetle-like or distinctly robotic, lacking the natural fluidity characteristic of mammalian movements. Existing literature on quadrupedal robots with spinal configurations primarily focuses on energy efficiency and does not consider the effects in human-robot interaction scenarios. Our contributions include an initial investigation into various trajectory generation strategies for a quadrupedal robot with a four degree of freedom spine, and an analysis on the effect that such methods have on human perception of gait naturalness compared to a fixed spine baseline. The strategies were evaluated using videos of walking, trotting and turning simulations. Among the four different strategies developed, the optimised time varying and the foot-tracking strategies were perceived to be more natural than the baseline in a randomised trial with 50 participants. Although none of the strategies demonstrated any energy efficiency improvements over the no-spine baseline, some showed greater footfall consistency at higher speeds. Given the greater likeability drawn from the more natural locomotion patterns, this type of robot displays potential for applications in social robot scenarios such as elderly care, where energy efficiency is not a primary concern.

I Know Your Feelings Before You Do: Predicting Future Affective Reactions in Human-Computer Dialogue

Current Spoken Dialogue Systems (SDSs) often serve as passive listeners that respond only after receiving user speech. To achieve human-like dialogue, we propose a novel future prediction architecture that allows an SDS to anticipate future affective reactions based on its current behaviors before the user speaks. In this work, we investigate two scenarios: speech and laughter. In speech, we propose to predict the user's future emotion based on its temporal relationship with the system's current emotion and its causal relationship with the system's current Dialogue Act (DA). In laughter, we propose to predict the occurrence and type of the user's laughter using the system's laughter behaviors in the current turn. Preliminary analysis of human-robot dialogue demonstrated synchronicity in the emotions and laughter displayed by the human and robot, as well as DA-emotion causality in their dialogue. This verifies that our architecture can contribute to the development of an anticipatory SDS.