Socially Distributed Perception: GRACE plays social tag at AAAI 2005

Michalowski, Marek P.; Šabanović, Selma; DiSalvo, Carl; Busquets, Dídac; Hiatt, Laura M.; Melchior, Nik A.; Simmons, Reid

doi:10.1007/s10514-006-9015-6

Cited by 35 publications

(17 citation statements)

References 14 publications

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“…We correlated our results to existing work that maps data points on the arousalvalence space to affective adjectives [18], as a means of generating loose-yetinformative keywords to roughly describe how various tail configurations may be perceived. We took the average rating for each motion and correlated it with the closest point on the previous work.…”

Section: Correlating Tail Motions To Affective Adjectivesmentioning

confidence: 99%

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A Dog Tail for Utility Robots: Exploring Affective Properties of Tail Movement

Singh

Young

2013

Lecture Notes in Computer Science

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Abstract. We present a dog-tail interface for utility robots, as a means of communicating high-level robotic state through affect. This interface leverages people's general knowledge of dogs and their tails (e.g., wagging means happy) to communicate robotic state in an easy to understand way. In this paper, we present the details of our tail construction, and the results of a study which explored a base case of people's reactions to the tail: how various parameters of tail movements and configuration influence perception of the robot's zoomorphized affective state. Our study indicated that people were able to interpret a range of affective states from various tail configurations and gestures, and in summary, we present a set of guidelines for mapping tail parameters to intended perceived affective robotic state.

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Section: Correlating Tail Motions To Affective Adjectivesmentioning

confidence: 99%

“…Some have suggested the use of facial expressions and embodied gestures, where examples include mechanized faces with eyebrows, mouths, etc. [1][2][3]34], animated faces on screens [16,18], using mixed reality to superimpose graphics faces on robots [31], humanlike whole gestures with arms, etc. [2], or even using gaze [29].…”

Section: Related Workmentioning

confidence: 99%

A Dog Tail for Utility Robots: Exploring Affective Properties of Tail Movement

Singh

Young

2013

Lecture Notes in Computer Science

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“…For one, robotic systems that use natural language for robot instruction either do not have natural language fully integrated into the robotic architecture (for example, Michalowski et al [2007]) or are limited to simple instructions (for example, Firby [1989], Atrash et al [2009]). And many systems use rule-based grammars due to the difficulty of producing a well-trained grammar on what is invariably the small amount of data applicable to the domain.…”

Section: Previous Workmentioning

confidence: 99%

Toward Humanlike Task‐Based Dialogue Processing for Human Robot Interaction

2011

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ArticlesWINTER 2011 77 I nteractions in natural language dialogues are an essential part of human social exchanges, ranging from social conventions such as greetings, to simple question-answer pairs, to task-based dialogues for coordinating activities, topic-based discussions, and all kinds of more open-ended conversations. As a result, the ability of future social and service robots to interact with humans in natural ways ) will critically depend on developing capabilities of humanlike dialoguebased natural language processing (NLP) in robotic architectures. However, different from other NLP contexts such as story understanding or machine translation, natural language processing on robots has at least the following six properties: realtime, parallel, spoken, embodied, situated, and dialogue-based.Real-time means that all processing must occur within the time frame of human processing, both at the level of comprehension as well as production. It also means that constraints will have to be incorporated incrementally as they occur, analogous to human language processing.Parallel means that all stages of language processing must operate concurrently to mutually constrain possible meaning interpretations and to allow for the generation of responses (such as acknowledgements) while an ongoing utterance is being processed.Spoken means that language processing necessarily operates on imperfect acoustic signals with varying quality that depends on the speaker and the background noise. In addition to handling prosodic variations, this includes typical features of spontaneous speech such as various types of disfluencies, slips of the tongue, or other types of errors that are usually not found in written texts.Embodied means that robots have to be able to process multimodal linguistic cues such as deictic terms accompanied by bodily movements, or other gestures that constrain possible interpretations of linguistic expressions. It also means that the robot will have to be able to produce similar gestures that are expected by human interlocutors to accompany certain linguistic constructs.

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“…Most approaches to natural language understanding on robots (e.g., [1]- [4]) are sequential, make limited use of context, task knowledge, and goal structures, and ignore physical aspects of language users. In contrast, converging evidence from psycholinguistics suggests that human language understanding is incremental and parallel, depends on the speaker's and listener's contexts, utilizes task and goal knowledge, and involves the perceptions and perspectives of situated, embodied agents.…”

Section: Introductionmentioning

confidence: 99%

Robust spoken instruction understanding for HRI

Cantrell¹,

Scheutz²,

Schermerhorn³

et al. 2010

Proceeding of the 5th ACM/IEEE International Conference on Human-Robot Interaction - HRI '10

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Abstract-Natural human-robot interaction requires different and more robust models of language understanding (NLU) than non-embodied NLU systems. In particular, architectures are required that (1) process language incrementally in order to be able to provide early backchannel feedback to human speakers; (2) use pragmatic contexts throughout the understanding process to infer missing information; and (3) handle the underspecified, fragmentary, or otherwise ungrammatical utterances that are common in spontaneous speech. In this paper, we describe our attempts at developing an integrated natural language understanding architecture for HRI, and demonstrate its novel capabilities using challenging data collected in human-human interaction experiments.

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Socially Distributed Perception: GRACE plays social tag at AAAI 2005

Cited by 35 publications

References 14 publications

A Dog Tail for Utility Robots: Exploring Affective Properties of Tail Movement

A Dog Tail for Utility Robots: Exploring Affective Properties of Tail Movement

Toward Humanlike Task‐Based Dialogue Processing for Human Robot Interaction

Robust spoken instruction understanding for HRI

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