Evidence map›Paper›PMID 30586407›Full record

ArticlePloS one2018

Modelling the structure of object-independent human affordances of approaching to grasp for robotic hands.

Giuseppe Cotugno, Jelizaveta Konstantinova, Kaspar Althoefer, Thrishantha Nanayakkara

Abstract read
In one paragraph

Article in PloS one, 2018. 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. 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

4 authors.

Giuseppe CotugnoCentre for Robotics Research, Department of Informatics, King's College London, Strand, WC2R 2LS, London, United Kingdom.ORCID 0000-0002-1545-5513
Jelizaveta KonstantinovaCentre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, Mile End Road, E1 4NS, London, United Kingdom.
Kaspar AlthoeferCentre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, Mile End Road, E1 4NS, London, United Kingdom.
Thrishantha NanayakkaraDyson School of Design Engineering, Imperial College London, South Kensington Campus, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Grasp affordances in robotics represent different ways to grasp an object involving a variety of factors from vision to hand control. A model of grasp affordances that is able to scale across different objects, features and domains is needed to provide robots with advanced manipulation skills. The existing frameworks, however, can be difficult to extend towards a more general and domain independent approach. This work is the first step towards a modular implementation of grasp affordances that can be separated into two stages: approach to grasp and grasp execution. In this study, human experiments of approaching to grasp are analysed, and object-independent patterns of motion are defined and modelled analytically from the data. Human subjects performed a specific action (hammering) using objects of different geometry, size and weight. Motion capture data relating the hand-object approach distance was used for the analysis. The results showed that approach to grasp can be structured in four distinct phases that are best represented by non-linear models, independent from the objects being handled. This suggests that approaching to grasp patterns are following an intentionally planned control strategy, rather than implementing a reactive execution.

Indexed as

Models, TheoreticalRoboticsFemaleHandHand StrengthHumansMaleNonlinear Dynamics

Identifiers

PMID30586407
PMCPMC6306220

What OpenQuestion holds

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LicenceCC BY
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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.