Abstract
We hypothesize that the initiative of a robot during a collaborative task with a human can influence the pace of interaction, the human response to attention cues, and the perceived engagement. We propose an object learning experiment where the human interacts in a natural way with the humanoid iCub. Through a two-phases scenario, the human teaches the robot about the properties of some objects. We compare the effect of the initiator of the task in the teaching phase (human or robot) on the rhythm of the interaction in the verification phase. We measure the reaction time of the human gaze when responding to attention utterances of the robot. Our experiments show that when the robot is the initiator of the learning task, the pace of interaction is higher and the reaction to attention cues faster. Subjective evaluations suggest that the initiating role of the robot, however, does not affect the perceived engagement. Moreover, subjective and third-person evaluations of the interaction task suggest that the attentive mechanism we implemented in the humanoid robot iCub is able to arouse engagement and make the robot's behavior readable.
Highlights
Personal robots and robotic co-workers need to interact with humans and coordinate with them to fulfill collaborative tasks
We study the effects of a simple joint attention system during a learning task, in terms of induced joint attention and engagement perceived on the human side
DOES AN ACTIVE ROBOT INDUCE A FASTER INTERACTION? The response times reported in Table 1 show that humans respond faster to robot’s utterances in the verification phase when in the previous phase of the task the robot was leading the interaction (RI condition)
Summary
Personal robots and robotic co-workers need to interact with humans and coordinate with them to fulfill collaborative tasks. The robot needs the capability to learn from him as an intelligent social partner (Breazeal, 2003): it must be endowed with tools for learning new skills and symbols, that the human can teach physically and verbally, and with social skills to interact with humans in a way that is as easy and natural as possible (Huang and Thomaz, 2011; Knoblich et al, 2011). A critical component for natural Human–Robot Interaction (HRI) is the selection and implementation of the social skills that can make the robot “interactable,” i.e., usable and understandable by ordinary people. These skills translate in a combination of verbal and non-verbal communication, which must be adapted in real-time to each human behavior. Implicit non-verbal communication positively impacts humanrobot tasks performance (Breazeal et al, 2005)
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