Demo Data from EDBT 2026 / reviewers in the wild / expert
M. Reza Motamedi
dblp:166/6367
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Haptics and multimodal interaction › haptic device control
grasp force control |
0.2 | 1 | 2016 | Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016 |
Haptics and multimodal interaction
haptic feedback |
0.2 | 1 | 2016 | Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016 |
Human-robot interaction
teleoperation |
0.2 | 1 | 2016 | 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 |
0.2 | 1 | 2016 | Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016 |
Robotics › Robot manipulation
grasping |
0.1 | 1 | 2016 | Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasks · ICRA 2016 |
Robotics › Robot manipulation › tactile sensing
slip detection |
0.1 | 1 | 2016 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Haptic feedback for improved robotic arm control during simple grasp, slippage, and contact detection tasksabstractThis 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 |
ICRA | 1 |
| 2015 | Tactile sensation transmission from a robotic arm to the human body via a haptic interfaceabstractThis 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 Haptics | 1 |