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M. Reza Motamedi

dblp:166/6367 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2016
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

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 · 75% Human-robot interaction · 25%
Artificial intelligence
1 paper
Robot manipulation · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction › haptic device control
grasp force control
0.212016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016
Haptics and multimodal interaction
haptic feedback
0.212016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016
Human-robot interaction
teleoperation
0.212016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016
Haptics and multimodal interaction › haptic feedback
vibrotactile feedback
0.212016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016
Robotics › Robot manipulation
grasping
0.112016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016
Robotics › Robot manipulation › tactile sensing
slip detection
0.112016
Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016

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

user study · 0.5
YearPublicationVenuePosition
2016 Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks
abstract
This paper explores the use of haptic and visual feedback during routine object-manipulation tasks. We conducted tests of slippage detection, contact detection, and grasp control, with the help of twelve human participants. During the tests, the subjects controlled the robotic arm of a UR5 Universal Robot that was mounted with a three-finger robotic gripper and equipped with tactile sensors. The participants used either their visual feedback, vibrotactile feedback, or pressure feedback to assess the movements of the robotic fingers as they attempted to complete each task. We analyzed their performance with each type of feedback in order to evaluate how haptic feedback may be used to reduce the need for visual attention, and thus improve the lives of upper-limb amputees.
M. Reza Motamedi, Jean-Baptiste Chossat, Jean-Philippe Roberge, Vincent Duchaine
ICRA1
2015 Tactile sensation transmission from a robotic arm to the human body via a haptic interface
abstract
This paper presents a robotic system that was used to study the restoration of touch sensitivity. This approach could improve the lives of people who suffer from having lost organs or upper-limbs. Here, a combination of tactile sensors, robotic fingers, and a haptic interface enabled us to undertake different types of experiments on human subjects. To this end, we have conducted two separate tests on eight human subjects in order to assess the effectiveness of the static and dynamic modalities in different detectable ranges of the skin sensitivity. As of now, overall results show the relative functionality of the proposed mechanism for further experimental research. Under a static condition we received better restitution feedback at the lowest and highest magnitude levels, compared to the two in-between levels. The lowest and highest magnitude levels, 2N and 8N respectively, had an overall success rate of 75%, while the two middle levels, 4N and 6N, had a success rate of 43%. Under a dynamic condition, results showed that the whole system could adequately convey texture information via vibrations, and that it allowed subjects to successfully differentiate textures 78.33% of the time.
M. Reza Motamedi, Jean-Philippe Roberge, Vincent Duchaine
World Haptics1