Edge Accelerated AI for Robotic Teleoperation

Alphonsus Keary; Nehal Amer; Masoud Emam; Alan Dunne; Javier Torres; Kate O'Riordan; Michael Walsh; Brendan O'Flynn

doi:10.1109/SENSORS59705.2025.11330622

Edge Accelerated AI for Robotic Teleoperation

Alphonsus Keary
, Nehal Amer
, Masoud Emam
, Alan Dunne
, Javier Torres
, Kate O'Riordan
, Michael Walsh
, Brendan O'Flynn

Analog Devices, Inc.
Emdalo Technologies

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

This paper presents a body of work on the development of edge accelerated AI hardware and software components related to robotic teleoperations. In particular, the paper proposes a system based architecture incorporating teleoperations communications layers in the form of a local to remote edge based stack, along with a Time of Flight (ToF) sensor layer, delivering user controls via real time hand and gesture signals for robotic teleoperations. Typical use cases include, remote skills delivery, precision robot manipulation, factory of the future, dangerous work environments and many other Industry 4.0/5.0 scenarios.

Original language	English
Title of host publication	IEEE SENSORS 2025 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798331544676
DOIs	https://doi.org/10.1109/SENSORS59705.2025.11330622
Publication status	Published - 2025
Event	2025 IEEE SENSORS - Vancouver, Canada Duration: 19 Oct 2025 → 22 Oct 2025

Publication series

Name	Proceedings of IEEE Sensors
ISSN (Print)	1930-0395
ISSN (Electronic)	2168-9229

Conference

Conference	2025 IEEE SENSORS
Country/Territory	Canada
City	Vancouver
Period	19/10/25 → 22/10/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Artificial Intelligence
Edge Computing
Industrial Automation
Industry 4.0
Industry 5.0
PLCs
Profinet
RapID

Access to Document

10.1109/SENSORS59705.2025.11330622

Cite this

@inproceedings{4b946cd1a1fa44b1bed729dbb2d31764,

title = "Edge Accelerated AI for Robotic Teleoperation",

abstract = "This paper presents a body of work on the development of edge accelerated AI hardware and software components related to robotic teleoperations. In particular, the paper proposes a system based architecture incorporating teleoperations communications layers in the form of a local to remote edge based stack, along with a Time of Flight (ToF) sensor layer, delivering user controls via real time hand and gesture signals for robotic teleoperations. Typical use cases include, remote skills delivery, precision robot manipulation, factory of the future, dangerous work environments and many other Industry 4.0/5.0 scenarios.",

keywords = "Artificial Intelligence, Edge Computing, Industrial Automation, Industry 4.0, Industry 5.0, PLCs, Profinet, RapID",

author = "Alphonsus Keary and Nehal Amer and Masoud Emam and Alan Dunne and Javier Torres and Kate O'Riordan and Michael Walsh and Brendan O'Flynn",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE SENSORS ; Conference date: 19-10-2025 Through 22-10-2025",

year = "2025",

doi = "10.1109/SENSORS59705.2025.11330622",

language = "English",

series = "Proceedings of IEEE Sensors",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE SENSORS 2025 - Conference Proceedings",

address = "United States",

}

TY - GEN

T1 - Edge Accelerated AI for Robotic Teleoperation

AU - Keary, Alphonsus

AU - Amer, Nehal

AU - Emam, Masoud

AU - Dunne, Alan

AU - Torres, Javier

AU - O'Riordan, Kate

AU - Walsh, Michael

AU - O'Flynn, Brendan

PY - 2025

Y1 - 2025

N2 - This paper presents a body of work on the development of edge accelerated AI hardware and software components related to robotic teleoperations. In particular, the paper proposes a system based architecture incorporating teleoperations communications layers in the form of a local to remote edge based stack, along with a Time of Flight (ToF) sensor layer, delivering user controls via real time hand and gesture signals for robotic teleoperations. Typical use cases include, remote skills delivery, precision robot manipulation, factory of the future, dangerous work environments and many other Industry 4.0/5.0 scenarios.

AB - This paper presents a body of work on the development of edge accelerated AI hardware and software components related to robotic teleoperations. In particular, the paper proposes a system based architecture incorporating teleoperations communications layers in the form of a local to remote edge based stack, along with a Time of Flight (ToF) sensor layer, delivering user controls via real time hand and gesture signals for robotic teleoperations. Typical use cases include, remote skills delivery, precision robot manipulation, factory of the future, dangerous work environments and many other Industry 4.0/5.0 scenarios.

KW - Artificial Intelligence

KW - Edge Computing

KW - Industrial Automation

KW - Industry 4.0

KW - Industry 5.0

KW - PLCs

KW - Profinet

KW - RapID

UR - https://www.scopus.com/pages/publications/105034061463

U2 - 10.1109/SENSORS59705.2025.11330622

DO - 10.1109/SENSORS59705.2025.11330622

M3 - Conference proceeding

AN - SCOPUS:105034061463

T3 - Proceedings of IEEE Sensors

BT - IEEE SENSORS 2025 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 IEEE SENSORS

Y2 - 19 October 2025 through 22 October 2025

ER -

Edge Accelerated AI for Robotic Teleoperation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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