Turn Taking Based on Information Flow for Fluent Human‐Robot Interaction

Thomaz, Andrea L.; Chao, Crystal

doi:10.1609/aimag.v32i4.2379

Cited by 34 publications

(13 citation statements)

References 17 publications

(24 reference statements)

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“…contrast to the interactionally simultaneous setup in which motor interference is typically measured, interactional fluidity and motor synchronization can be measured and discussed based on turn-based interaction formats that resemble more closely the way humans naturally interact with one another [13,45]. The following thought experiment may help to illustrate the issue.…”

Section: Unresolved Issues Surrounding Motor Interference Measuresmentioning

confidence: 99%

Untitled

2019

ACM Trans. Hum.-Robot Interact.

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Motor resonance, the activation of an observer's motor control system by another actor's movements, has been claimed to be an indicator for quality of interaction. Motor interference as one of the consequences of the presence of resonance can be detected by analyzing an actor's spatial movements. It has therefore been used as an indicator for the presence of motor resonance. Unfortunately, the experimental paradigm in which motor interference has been shown to be detectable is ecologically implausible both in terms of the types of movements employed and the number of repetitions required. In the presented experiment, we tested whether some of these experimental constraints can be relaxed or modified toward a more naturalistic behavior without losing the ability to detect the interference effect. In the literature, spatial variance has been analytically quantified in many different ways. This study found these analytical variations to be nonequivalent by implementing them. Back-and-forth transitive movements were tested for motor interference; the effect was found to be more robust than with left-right movements, although the direction of interference was opposite to that reported in the literature. We conclude that motor interference, when measured by spatial variation, lacks promise for embedding in naturalistic interaction scenarios because the effect sizes were small. CCS Concepts: • Human-centered computing → User studies; Collaborative and social computing design and evaluation methods; Interactive systems and tools; Empirical studies in interaction design; Systems and tools for interaction design;

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Section: Unresolved Issues Surrounding Motor Interference Measuresmentioning

confidence: 99%

Untitled

2019

ACM Trans. Hum.-Robot Interact.

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“…Kühnlenz et al (2013) regret that social robots are designed to passively mimic human behavior instead of being responsive and react to behavior on-line. For instance, sometimes robots continue taking turns without realizing that the human did not respond (Thomaz and Chao 2012). Indeed, although the technology has been developed to help people, people have to adjust to the technology because it is not well-adapted to human preferences and conditions for interaction.…”

Section: From Tool To Collaboratormentioning

confidence: 99%

Mutual Recognition in Human-Robot Interaction: a Deflationary Account

Brinck

Balkenius

2018

Philos. Technol.

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Mutually adaptive interaction involves the robot as a partner as opposed to a tool, and requires that the robot is susceptible to similar environmental cues and behavior patterns as humans are. Recognition, or the acknowledgement of the other as individual, is fundamental to mutually adaptive interaction between humans. We discuss what recognition involves and its behavioral manifestations, and describe the benefits of implementing it in HRI.

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“…Our analysis of turn-taking breakdowns revealed the need for the robot to interrupt its actions when they were no longer relevant, or to yield the speaking floor during simultaneous turn bids. A subsequent autonomous implementation of such self-interruptions in Thomaz and Chao (2011) led to a faster paced interaction.…”

Section: Relation To Previous Workmentioning

confidence: 99%

Timed Petri nets for fluent turn-taking over multimodal interaction resources in human-robot collaboration

Chao

Thomaz

2016

The International Journal of Robotics Research

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The goal of this work is to develop computational models of social intelligence that enable robots to work side by side with humans, solving problems and achieving task goals through dialogue and collaborative manipulation. A defining problem of collaborative behavior in an embodied setting is the manner in which multiple agents make use of shared resources. In a situated dialogue, these resources include physical bottlenecks such as objects or spatial regions, and cognitive bottlenecks such as the speaking floor. For a robot to function as an effective collaborative partner with a human, it must be able to seize and yield such resources appropriately according to social expectations. We describe a general framework that uses timed Petri nets for the modeling and execution of robot speech, gaze, gesture, and manipulation for collaboration. The system dynamically monitors resource requirements and availability to control real-time turn-taking decisions over resources that are shared with humans, reasoning about different resource types independently. We evaluate our approach with an experiment in which our robot Simon performs a collaborative assembly task with 26 different human partners, showing that the multimodal reciprocal approach results in superior task performance, fluency, and balance of control.

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Turn Taking Based on Information Flow for Fluent Human‐Robot Interaction

Cited by 34 publications

References 17 publications

Untitled

Untitled

Mutual Recognition in Human-Robot Interaction: a Deflationary Account

Timed Petri nets for fluent turn-taking over multimodal interaction resources in human-robot collaboration

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