Evidence map›Paper›PMID 39460184›Full record

ArticleSensors (Basel, Switzerland)2024

Development of Dual-Arm Human Companion Robots That Can Dance.

Joonyoung Kim, Taewoong Kang, Dongwoon Song, Gijae Ahn, Seung-Joon Yi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Low-cost social robot designs for education: a review.Frontiers in robotics and AI · 2026
    Pooled it
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.

Joonyoung KimElectrical Engineering Department, Pusan National University, Busan 43241, Republic of Korea.ORCID 0000-0001-8184-7059
Taewoong KangElectrical Engineering Department, Pusan National University, Busan 43241, Republic of Korea.ORCID 0000-0001-8018-584X
Dongwoon SongElectrical Engineering Department, Pusan National University, Busan 43241, Republic of Korea.ORCID 0000-0002-7011-4033
Gijae AhnElectrical Engineering Department, Pusan National University, Busan 43241, Republic of Korea.
Seung-Joon YiElectrical Engineering Department, Pusan National University, Busan 43241, Republic of Korea.ORCID 0000-0002-3700-4967

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human-robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

Indexed as

DancingRoboticsEquipment DesignGesturesHumansdancing robotdual-arm robotgesture-based interactionhuman–robot interaction

Identifiers

PMID39460184
PMCPMC11511178

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.