Evidence map›Paper›PMID 34660702›Full record

ArticleFrontiers in robotics and AI2021

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation.

Alessandro Carfì, Timothy Patten, Yingyi Kuang, Ali Hammoud, Mohamad Alameh, Elisa Maiettini, Abraham Itzhak Weinberg, Diego Faria, Fulvio Mastrogiovanni, Guillem Alenyà and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in robotics and AI, 2021. 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
2.1field-weighted citation impact, top 13% of its field
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, 22 citations in OpenAlex.

  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

14 authors at 9 institutions in 6 countries.

Alessandro CarfìDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Timothy PattenVision for Robotics Laboratory, Institut für Automatisierungs- und Regelungstechnik, Technische Universität Wien, Vienna, Austria.
Yingyi KuangRobotics, Vision and Intelligent Systems, College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom.
Ali HammoudInstitut des Systèmes Intelligents et de Robotique, Sorbonne Université, Paris, France.
Mohamad AlamehDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Elisa MaiettiniHumanoid Sensing and Perception, Istituto Italiano di Tecnologia, Genoa, Italy.
Abraham Itzhak WeinbergRobotics, Vision and Intelligent Systems, College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom.
Diego FariaRobotics, Vision and Intelligent Systems, College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom.
Fulvio MastrogiovanniDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Guillem AlenyàInstitut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain.
Lorenzo NataleHumanoid Sensing and Perception, Istituto Italiano di Tecnologia, Genoa, Italy.
Véronique PerdereauInstitut des Systèmes Intelligents et de Robotique, Sorbonne Université, Paris, France.
Markus VinczeVision for Robotics Laboratory, Institut für Automatisierungs- und Regelungstechnik, Technische Universität Wien, Vienna, Austria.
Aude BillardLearning Algorithms and Systems Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
University of Genoa · ITAston University · GBInstitut Systèmes Intelligents et de Robotique · FRItalian Institute of Technology · ITÉcole Polytechnique Fédérale de Lausanne · CHInstitut de Robòtica i Informàtica Industrial · ESSorbonne Université · FRTU Wien · ATUniversity of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.

Indexed as

anthropomorphic handsdata extractiongraspinghand-object interactionimitation learninglearning from demonstrationmanipulationtransfer learning

Identifiers

PMID34660702
PMCPMC8517111
OpenAlexW3203092975

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.