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Stefan Reither

dblp:304/4278 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—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.

Artificial intelligence
1 paper
Robot manipulation · 75% Motion planning and robot control · 25%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
disassembly
0.512021
The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks · ICRA 2021
Robotics › Robot manipulation
grasping
0.512021
The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks · ICRA 2021
Robotics › Robot manipulation › disassembly
robotic disassembly
0.512021
The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks · ICRA 2021
Robotics › Motion planning and robot control
robot learning
0.512021
The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks · ICRA 2021

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

torque estimation · 0.5movement primitives · 0.5image-based visual servoing · 0.5
YearPublicationVenuePosition
2021 The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks
abstract
We introduce a multi-functional robotic gripper equipped with a set of actions required for disassembly of electromechanical devices. The gripper consists of a robot arm with 5 degrees of freedom (DoF) for manipulation and a jaw gripper with a 1-DoF rotation joint and a 1-DoF closing joint. The system enables manipulation in 7 DoF and offers the ability to reposition objects in hand and to perform tasks that usually require bimanual systems. The sensor system of the gripper includes relative and absolute joint encoders, force and pressure sensors to provide feedback about interaction forces, a tool- mounted camera for screw detection and precise placement of the tool tip using image-based visual servoing. We present a data-driven method for estimating joint torques based on the output voltage and motor speed. Further, we provide methods for teaching disassembly actions based on human demonstration, their representation as movement primitives and execution based on sensory feedback. We provide quantitative results regarding positioning and torque estimation accuracy, disassembly success rate and qualitative results regarding the successful disassembly of hard disc drives.
Cornelius Klas, Felix Hundhausen, Jianfeng Gao 0002, Christian R. G. Dreher, Stefan Reither, You Zhou 0007, Tamim Asfour
ICRA5