Evidence map›Paper›PMID 39722792›Full record

ArticleFrontiers in robotics and AI2024

Advancing teleoperation for legged manipulation with wearable motion capture.

Chengxu Zhou, Yuhui Wan, Christopher Peers, Andromachi Maria Delfaki, Dimitrios Kanoulas

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. 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
–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. 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

5 authors.

Chengxu ZhouIntelligent Robotics Research Group, Department of Computer Science, University College London, London, United Kingdom.
Yuhui WanSchool of Mechanical Engineering, University of Leeds, Leeds, United Kingdom.
Christopher PeersSchool of Mechanical Engineering, University of Leeds, Leeds, United Kingdom.
Andromachi Maria DelfakiIntelligent Robotics Research Group, Department of Computer Science, University College London, London, United Kingdom.
Dimitrios KanoulasIntelligent Robotics Research Group, Department of Computer Science, University College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The sanctity of human life mandates the replacement of individuals with robotic systems in the execution of hazardous tasks. Explosive Ordnance Disposal (EOD), a field fraught with mortal danger, stands at the forefront of this transition. In this study, we explore the potential of robotic telepresence as a safeguard for human operatives, drawing on the robust capabilities demonstrated by legged manipulators in diverse operational contexts. The challenge of autonomy in such precarious domains underscores the advantages of teleoperation-a harmonious blend of human intuition and robotic execution. Herein, we introduce a cost-effective telepresence and teleoperation system employing a legged manipulator, which combines a quadruped robot, an integrated manipulative arm, and RGB-D sensory capabilities. Our innovative approach tackles the intricate challenge of whole-body control for a quadrupedal manipulator. The core of our system is an IMU-based motion capture suit, enabling intuitive teleoperation, augmented by immersive visual telepresence via a VR headset. We have empirically validated our integrated system through rigorous real-world applications, focusing on loco-manipulation tasks that necessitate comprehensive robot control and enhanced visual telepresence for EOD operations.

Indexed as

human-robot interactionlegged robotsmobile manipulationteleoperationtelexistencewhole-body control

Identifiers

PMID39722792
PMCPMC11668679

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.