Evidence map›Paper›PMID 34738017›Full record

ArticleFrontiers in robotics and AI2021

Comparative Analysis of Model-Based Predictive Shared Control for Delayed Operation in Object Reaching and Recognition Tasks With Tactile Sensing.

Leone Costi, Luca Scimeca, Perla Maiolino, Thilina Dulantha Lalitharatne, Thrishantha Nanayakkara, Ryman Hashem, Fumiya Iida

Open access · goldAbstract read
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

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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

7 authors at 5 institutions in 2 countries.

Leone CostiBio Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Luca ScimecaNAVER AI Lab, NAVER Corp, Seongnam-si, South Korea.
Perla MaiolinoOxford Robotics Institute, University of Oxford, Oxford, United Kingdom.
Thilina Dulantha LalitharatneDyson School of Design Engineering, Imperial College London, London, United Kingdom.
Thrishantha NanayakkaraDyson School of Design Engineering, Imperial College London, London, United Kingdom.
Ryman HashemBio Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Fumiya IidaBio Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
University of Cambridge · GBDyson (United Kingdom) · GBImperial College London · GBNaver (South Korea) · KRUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user's awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot's motion. We propose shared control as a tool to compensate and mitigate the effects of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyse the effects of added delay and propose a unilateral teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic sensing. We propose four different control modalities of increasing autonomy:

Indexed as

bayesian predictorcommunication delayhuman-robot collaborationshared controlteleoperation

Identifiers

PMID34738017
PMCPMC8562425
OpenAlexW3202877510

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.