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Katrene Morozov

dblp:342/1689 · DBLP profile ↗
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1ranked-venue papers
0as 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 since 2021Systems, architecture and hardware · 1 · 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
Wearable and physiological sensing · 77% Human-robot interaction · 23%
Artificial intelligence
1 paper
Robot manipulation · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
tactile sensing
0.712023
RobotSweater: Scalable, Generalizable, and Customizable Machine-Knitted Tactile Skins for Robots · ICRA 2023
Wearable and physiological sensing
pressure sensing
0.712023
RobotSweater: Scalable, Generalizable, and Customizable Machine-Knitted Tactile Skins for Robots · ICRA 2023
Human-robot interaction
physical human-robot interaction
0.212023
RobotSweater: Scalable, Generalizable, and Customizable Machine-Knitted Tactile Skins for Robots · ICRA 2023

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

textile manufacturing · 1.3machine knitting · 1.3
YearPublicationVenuePosition
2023 RobotSweater: Scalable, Generalizable, and Customizable Machine-Knitted Tactile Skins for Robots
abstract
Tactile sensing is essential for robots to perceive and react to the environment. However, it remains a challenge to make large-scale and flexible tactile skins on robots. Industrial machine knitting provides solutions to manufacture customiz-able fabrics. Along with functional yarns, it can produce highly customizable circuits that can be made into tactile skins for robots. In this work, we present RobotSweater, a machine-knitted pressure-sensitive tactile skin that can be easily applied on robots. We design and fabricate a parameterized multi-layer tactile skin using off-the-shelf yarns, and characterize our sensor on both a flat testbed and a curved surface to show its robust contact detection, multi-contact localization, and pressure sensing capabilities. The sensor is fabricated using a well-established textile manufacturing process with a programmable industrial knitting machine, which makes it highly customizable and low-cost. The textile nature of the sensor also makes it easily fit curved surfaces of different robots and have a friendly appearance. Using our tactile skins, we conduct closed-loop control with tactile feedback for two applications: (1) human lead-through control of a robot arm, and (2) human-robot interaction with a mobile robot.
Zilin Si, Tianhong Catherine Yu, Katrene Morozov, James McCann, Wenzhen Yuan 0001
ICRA3