Vision-Based Human-Robot Handover System with Reinforcement Learning.

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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.

Weiliang CaoSchool of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia.ORCID 0009-0004-4159-560X

Zhenwei CaoSchool of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia.ORCID 0000-0002-6910-7346

Yong SongSchool of Airspace Science and Engineering, Shandong University, Weihai 264209, China.ORCID 0000-0003-2505-2766

Funding

Australian Research Council DP190101557

6 · The paper itself

Abstract

Handover control in human-robot collaboration remains a significant challenge. This paper proposes a three-step vision-based human-robot handover system (VHS). Vision inputs are used to perceive the environment and enable adaptive control of the robotic arm. Moreover, a three-step behavior cloning learning strategy is designed. Furthermore, a modified Temporal Difference (TD) loss function based on transfer models is proposed to train the algorithm to improve policy exploration and convergence. The proposed method results in substantial enhancements in comparative experimental validation in a simulation environment with a realistic dynamic hand model.

Indexed as

RoboticsAlgorithmsArtificial IntelligenceComputer SimulationHumansIntelligent SystemsReinforcement Machine LearningSoft Computingcollaborationhandoverhuman–robotsimulationvision-based

Identifiers

PMID42356784

DOI10.3390/s26123811

PMCPMC13306604

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.