Evidence map›Paper›PMID 38719859›Full record

ArticleScientific reports2024

Closing the loop in minimally supervised human-robot interaction: formative and summative feedback.

Mayumi Mohan, Cara M Nunez, Katherine J Kuchenbecker

Abstract read
In one paragraph

Article in Scientific reports, 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. Review
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

3 authors.

Mayumi MohanHaptic Intelligence Department, Max Planck Institute for Intelligent Systems, 70569, Stuttgart, Germany. maymohan@is.mpg.de.
Cara M NunezHaptic Intelligence Department, Max Planck Institute for Intelligent Systems, 70569, Stuttgart, Germany.
Katherine J KuchenbeckerHaptic Intelligence Department, Max Planck Institute for Intelligent Systems, 70569, Stuttgart, Germany. kjk@is.mpg.de.

Funding

Deutsche Forschungsgemeinschaft Germany's Excellence Strategy - EXC 2120/1 - 390831618Deutscher Akademischer Austausdienst Research Grants - One-Year Grants 57440918National Science Foundation Graduate Research Fellowship Program
6 · The paper itself

Abstract

Human instructors fluidly communicate with hand gestures, head and body movements, and facial expressions, but robots rarely leverage these complementary cues. A minimally supervised social robot with such skills could help people exercise and learn new activities. Thus, we investigated how nonverbal feedback from a humanoid robot affects human behavior. Inspired by the education literature, we evaluated formative feedback (real-time corrections) and summative feedback (post-task scores) for three distinct tasks: positioning in the room, mimicking the robot's arm pose, and contacting the robot's hands. Twenty-eight adults completed seventy-five 30-s-long trials with no explicit instructions or experimenter help. Motion-capture data analysis shows that both formative and summative feedback from the robot significantly aided user performance. Additionally, formative feedback improved task understanding. These results show the power of nonverbal cues based on human movement and the utility of viewing feedback through formative and summative lenses.

Indexed as

RoboticsAdultFeedbackFemaleFormative FeedbackHumansMaleYoung Adult

Identifiers

PMID38719859
PMCPMC11079071

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.