Evidence map›Paper›PMID 39328470›Full record

ArticleFrontiers in robotics and AI2024

Unraveling the thread: understanding and addressing sequential failures in human-robot interaction.

Lucien Tisserand, Brooke Stephenson, Heike Baldauf-Quilliatre, Mathieu Lefort, Frédéric Armetta

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Lucien TisserandInteractions, Corpus, Apprentissages, Représentations (ICAR) UMR5191, Centre National de la Recherche Scientifique, ENS de Lyon and Université Lyon 2, Labex ASLAN, Lyon, France.
Brooke StephensonInteractions, Corpus, Apprentissages, Représentations (ICAR) UMR5191, Centre National de la Recherche Scientifique, ENS de Lyon and Université Lyon 2, Labex ASLAN, Lyon, France.
Heike Baldauf-QuilliatreInteractions, Corpus, Apprentissages, Représentations (ICAR) UMR5191, Centre National de la Recherche Scientifique, ENS de Lyon and Université Lyon 2, Labex ASLAN, Lyon, France.
Mathieu LefortUniversity Lyon, Université Claude Bernard Lyon 1, CNRS, INSA Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) UMR5205, Villeurbanne, France.
Frédéric ArmettaUniversity Lyon, Université Claude Bernard Lyon 1, CNRS, INSA Lyon, Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS) UMR5205, Villeurbanne, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interaction is a dynamic process that evolves in real time. Participants interpret and orient themselves towards turns of speech based on expectations of relevance and social/conversational norms (that have been extensively studied in the field of Conversation analysis). A true challenge to Human Robot Interaction (HRI) is to develop a system capable of understanding and adapting to the changing context, where the meaning of a turn is construed based on the turns that have come before. In this work, we identify issues arising from the inadequate handling of the sequential flow within a corpus of in-the-wild HRIs in an open-world university library setting. The insights gained from this analysis can be used to guide the design of better systems capable of handling complex situations. We finish by surveying efforts to mitigate the identified problems from a natural language processing/machine dialogue management perspective.

Indexed as

contextualisationconversation analysisgroundinghuman-robot interactionin-the-wildsequentiality

Identifiers

PMID39328470
PMCPMC11426087

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.