Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration.

Ning Zhang, Tao Qi, Yongjia Zhao

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ning ZhangState Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0002-0127-1334

Tao QiState Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Yongjia ZhaoState Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Funding

The National Key Research and Development Program of China 2018AAA0102902

6 · The paper itself

Abstract

Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities demonstration, and using the motion data of the virtual demonstration system, the human demonstration is deduced into an activity sequence that can be performed by the robot. Through experimentation, the virtual reality demonstration system in this paper can achieve a 95% correct rate of activity recognition. We also created a simulated ur5 robotic arm grasping system to reproduce the inferred activity sequence.

Indexed as

Virtual RealityHumansLearningMotionRecognition, PsychologyUser-Computer Interfaceactivity recognitionlearn from demonstrationvirtual reality

Identifiers

PMID34577409

DOI10.3390/s21186201

PMCPMC8472799

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.