The impact of the contingency of robot feedback on HRI

Fischer, Kerstin; Lohan, Katrin Solveig; Saunders, Joe; Nehaniv, Chrystopher L.; Wrede, Britta; Rohlfing, Katharina J.

doi:10.1109/cts.2013.6567231

Cited by 40 publications

(15 citation statements)

References 41 publications

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“…Moreover, Kose-Bagci et al [2]find that human participants prefer interactions in which a humanoid robot's turn-taking behavior is driven by a contingency model whose temporal dynamics is the closest to 'natural' human-human conversations. Finally, experiments by Lohan et al [3] and Fischer et al [4] show that the timing of robot responses influences the extent to which users tutor the robot.A few studies furthermore address intuitive user behaviors in the tutoring of robots; for instance, Thomaz & Breazeal [5] analyze users" intuitive tutoring strategies and find that users" intuitive non-verbal tutoring strategies during learning from demonstration facilitate robot learning [6]. However, Chernova & Thomaz [7] in their recent review of robot learning from human teachers argue that users" behaviors in such tutoring situations have not been sufficiently studied; thus, what effects the timing of robot behaviors may have on the strategies human tutors choose is still open.…”

Section: Literature Reviewmentioning

confidence: 97%

Negotiating Instruction Strategies during Robot Action Demonstration

Jensen

Fischer

Shukla

et al. 2015

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

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This paper describes the kinds of strategies naïve users of an industrial robotic platform make use of and analyze how these strategies are adjusted based on the robot"s feedback. The study shows that users" actions are contingent on the robot"s response to such a degree that users will try out alternative instruction strategies if they do not see an effect in the robot within a time frame of two seconds. Thus, the timing of the robot"s actions (or in-actions) influences how users instruct the robot.

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Section: Literature Reviewmentioning

confidence: 97%

Negotiating Instruction Strategies during Robot Action Demonstration

Jensen

Fischer

Shukla

et al. 2015

Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

Self Cite

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“…Lastly, humans may not re- spond at all, because of a complete inability of the robot to communicate with nonverbal-behaviors or gestures. The synchrony of human-robot movements and the contingent response to reciprocal cues are critical features for evaluating the quality of imitation and joint attention [48,30]. Figure 8 highlights the joint attention elicited by the robot towards a human: in blue the yaw head movement of the human, in red the robot's.…”

Section: Dynamic Analysismentioning

confidence: 99%

Evaluating the Engagement with Social Robots

Anzalone

Boucenna

Ivaldi

et al. 2015

Int J of Soc Robotics

176

158

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To interact and cooperate with humans in their daily-life activities, robots should exhibit humanlike "intelligence". This skill will substantially emerge from the interconnection of all the algorithms used to ensure cognitive and interaction capabilities. While new robotics technologies allow us to extend such abilities, their evaluation for social interaction is still challenging. The quality of a human-robot interaction can not be reduced to the evaluation of the employed algorithms: we should integrate the engagement information that naturally arises during interaction in response to the robot's behaviors. In this paper we want to show a practical approach to evaluate the engagement aroused during interactions between humans and social robots. We will introduce a set of metrics useful in direct, face to face scenarios, based on the behaviors analysis of the human partners. We will show how such metrics are use

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“…The way in which tutors present an action is not only characterized by synchrony between talking and action, but also by the interpersonal coordination between tutor and learner [115].…”

Section: Models Of Cognitive Systemsmentioning

confidence: 99%

Embodied Language Learning and Cognitive Bootstrapping: Methods and Design Principles

Lyon

Nehaniv

Saunders

et al. 2016

International Journal of Advanced Robotic Systems

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Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results.Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel codevelopment of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.

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The impact of the contingency of robot feedback on HRI

Cited by 40 publications

References 41 publications

Negotiating Instruction Strategies during Robot Action Demonstration

Negotiating Instruction Strategies during Robot Action Demonstration

Evaluating the Engagement with Social Robots

Embodied Language Learning and Cognitive Bootstrapping: Methods and Design Principles

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