Robots Learning to Say “No”

Förster, Frank; Saunders, Joe; Lehmann, Hagen; Nehaniv, Chrystopher L.

doi:10.1145/3359618

Cited by 13 publications

(3 citation statements)

References 35 publications

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“…In support of this view are the following transcripts originating from the negation acquisition studies conducted by Förster, Saunders, Lehmann, and Nehaniv (2019). These studies consisted of multiple sessions per participant, and the transcripts pertains both to participant P12 (P) teaching object labels to Deechee (D), a childlike humanoid robot that was presented to participants as a young language learner.…”

Section: Introductionmentioning

confidence: 99%

Social robots as depictions of social agents

Clark¹,

Fischer²

2022

Behav Brain Sci

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Social robots serve people as tutors, caretakers, receptionists, companions, and other social agents. People know that the robots are mechanical artifacts, yet they interact with them as if they were actual agents. How is this possible? The proposal here is that people construe social robots not as social agents per se, but as depictions of social agents. They interpret them much as they interpret ventriloquist dummies, hand puppets, virtual assistants, and other interactive depictions of people and animals. Depictions as a class consist of three physical scenes with part-by-part mappings between them: (a) a base scene (the raw physical artifact), (b) the depiction proper (the artifact construed as a depiction), and (c) the scene depicted (the scene people are to imagine). With social robots, evidence shows, people form the same three scenes plus mappings: They perceive the raw machinery of a robot, construe it as a depiction of a character, and, using the depiction as a guide, engage in the pretense that they are interacting with the character depicted. So, with social robots, people also recognize three classes of agents—the characters depicted, the intended recipients of the depictions (those who view or interact with the robots), and the authorities responsible for the robots (the designers, makers, and owners). Construing social robots as depictions, we argue, accounts for many phenomena not covered by alternative models.

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Section: Introductionmentioning

confidence: 99%

Social robots as depictions of social agents

Clark¹,

Fischer²

2022

Behav Brain Sci

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“…The process of defining the types of errors could also help us to understand why they arise, measure their impact and explore possibilities and appropriate ways to detect, mitigate and recover from them. If, for example, artificial agents and human users are mismatched conversational partners as suggested by Moore (2007) and Förster et al (2019), and if this mismatch creates constraints and a "habitability gap" in HRI (Moore, 2017), are their specific types of failures that only occur due to such asymmetric setups? And, if yes, what does that mean for potential error management in HRI?…”

Section: Wanted: a Taxonomy Of Conversational Failures In Hrimentioning

confidence: 99%

Working with troubles and failures in conversation between humans and robots: workshop report

Förster,

Romeo,

Holthaus

et al. 2023

Front. Robot. AI

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This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on miscommunication from two complementary perspectives. One group of technology-oriented researchers was made up of roboticists, Human-Robot Interaction (HRI) researchers and dialogue system experts. The second group involved experts from conversation analysis, cognitive science, and linguistics. Uniting both groups of researchers is the belief that communication failures between humans and machines need to be taken seriously and that a systematic analysis of such failures may open fruitful avenues in research beyond current practices to improve such systems, including both speech-centric and multimodal interfaces. This workshop represents a starting point for this endeavour. The aim of the workshop was threefold: Firstly, to establish an interdisciplinary network of researchers that share a common interest in investigating communicative failures with a particular view towards robotic speech interfaces; secondly, to gain a partial overview of the “failure landscape” as experienced by roboticists and HRI researchers; and thirdly, to determine the potential for creating a robotic benchmark scenario for testing future speech interfaces with respect to the identified failures. The present article summarizes both the “failure landscape” surveyed during the workshop as well as the outcomes of the attempt to define a benchmark scenario.

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“…The potential of task-related aspects of negation in the context of explaining (the robot uses it for contrasting) in HRI has not been explored so far. Studies often focused on narrow aspects of negation, such as affect or volition, as context conditions ( Förster et al, 2019 ). Although recently the focus shifted towards explainable robots with some progress in the direction of explaining why robots reject commands of a human ( Scheutz et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue

Groß,

Singh,

Banh

et al. 2023

Front. Robot. AI

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Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts—measured as breaks in the finger movement data before the correct gesture was carried out—when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.

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Robots Learning to Say “No”

Cited by 13 publications

References 35 publications

Social robots as depictions of social agents

Social robots as depictions of social agents

Working with troubles and failures in conversation between humans and robots: workshop report

Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue

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