Abstract
Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.
Highlights
A substantial percentage of action taken by humans in their daily lives is social action—informing, promising, convincing, asking, teaching, and learning from others
Our results show that: (1) manipulating our floor regulation parameters results in different robot behavior; (2) people perceive this difference in behavior and attribute different personalities to the robot; and (3) changing the robot’s personality results in different behavior from the human, manipulating social dynamics of the dyad
This manipulation in robot behavior succeeded in eliciting different behavior from the human partner across the two groups, changing the social dynamics of the dyad
Summary
A substantial percentage of action taken by humans in their daily lives is social action—informing, promising, convincing, asking, teaching, and learning from others. Natural language and dialogue are constructs that exist to facilitate social action, and their sophistication in humans reflects a unique reliance upon social action to accomplish their goals (Clark, 1996). Turn-taking through situated dialogue is one of the most common protocols by which humans communicate and exchange information, which occurs naturally in humans from a very young age (Trevarthen, 1979; Tronick, Als, & Adamson, 1979). Turn-taking between humans and robots remains an awkward and confusing experience for human users. A reason for this continued awkwardness is that turn-taking is often relegated to the status of emergent behavior in human-robot interaction (HRI) systems, rather than treated as an interaction process to be explicitly controlled. If a robot does not have the capacity to adapt to the human’s
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