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Jorn Verheggen

dblp:353/5746 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 91% Human-robot interaction · 9%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction
haptic feedback
0.712023
KRIS: A Novel Device for Kinesthetic Corrective Feedback during Robot Motion · ICRA 2023
Haptics and multimodal interaction › haptic feedback
kinesthetic feedback
0.712023
KRIS: A Novel Device for Kinesthetic Corrective Feedback during Robot Motion · ICRA 2023
Haptics and multimodal interaction › haptic feedback
passive haptic feedback
0.712023
KRIS: A Novel Device for Kinesthetic Corrective Feedback during Robot Motion · ICRA 2023

Methods — techniques the papers use, named apart from their topics

user study · 0.7usability evaluation · 0.7
YearPublicationVenuePosition
2023 KRIS: A Novel Device for Kinesthetic Corrective Feedback during Robot Motion
abstract
This paper presents a novel device that can be used to perform kinesthetic corrective feedback for robotic systems. KRIS (Kinesthetic Robotic Interaction System) is a device that can be mounted on the end-effector of an articulated robot. From here it can be manipulated by a human to give corrective feedback to the robot system during execution and in an intuitive way. The device can provide feedback in six degrees of freedom while giving passive haptic feedback to the user about both the position, rotation, and movement of the robot. We evaluated KRIS in a user study with respect to a baseline based on keyboard feedback in the areas of usability, intuitiveness, accuracy of corrections, and user task load. KRIS outperformed our baseline on the first three metrics and performed similar on task load. We believe that KRIS can enable a wide variety of robots to be taught interactively by non-expert humans in diverse collaborative settings.
Jorn Verheggen, Kim Baraka
ICRA1