Demo Data from SIGIR 2026 / reviewers in the wild / expert
Amir Rabani
dblp:188/6926
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Embedded and real-time systems › cyber-physical systems › robot systems
soft robotics |
0.6 | 1 | 2022 | Continuum Robot Proprioception: The Ionic Liquid Approach · IEEE Trans. Robotics 2022 |
Embedded and real-time systems
cyber-physical system platforms |
0.2 | 1 | 2022 | Continuum Robot Proprioception: The Ionic Liquid Approach · IEEE Trans. Robotics 2022 |
Methods — techniques the papers use, named apart from their topics
sensor characterization · 0.6calibration · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Continuum Robot Proprioception: The Ionic Liquid ApproachabstractSlender continuum robots are uniquely placed to address inspection and intervention tasks in difficult-to-access environments. Tasks such as minimally invasive surgery andin situmaintenance of complex machinery require dexterous robots with accurate motion control, and benefit from instruments with small diameters and large internal passages for services. Intrinsic proprioception, or built-in position sensory feedback, has been described in prior work on continuum and soft robots but seldom employed for motion closed-loop control. In this research article, we present a novel tubular resistive stretch sensor with ionic liquid conductors to enable closed-loop position control of low-cost, 3-D printed continuum robots. We describe a methodology for sensor characterization and calibration drawing from a model of their behavior under strain, allowing us to apply four stretch sensors as proprioceptive feedback for motion control of a four-axis slender continuum manipulator. We demonstrate a substantial (approximately 65%) improvement in position control accuracy and repeatability over open-loop methods and show a promising step toward low-cost, scalable position feedback for slender continuum robots. David Alatorre, Dragos Axinte, Amir Rabani |
IEEE Trans. Robotics | 3 |
| 2018 | Robotic Boreblending: The Future of In-Situ Gas Turbine RepairabstractAutomation of inspection and repair tasks on complex installations is gaining attention from industries with high-value assets such as aerospace, nuclear and marine. This paper reports on a five degrees of freedom robotic system capable of performing accurate and repeatable repair procedures through a narrow inspection port, which minimizes the cost and downtime associated with unscheduled maintenance. Careful study of the target working volume and repair process informed the design of a robotic probe capable of replicating the operation. Kinematic analysis of the robot's flexible, prismatic and rotary joints was used to define accurate machining paths in 3D space, and the results were verified using an optical motion capture system (accuracy of 0.25 mm). After comprehensive verifications of the constitutive elements, the robotic system was successfully demonstrated for repair of a high-pressure compressor aerofoil in a gas turbine. The results not only proves the ability of the system to address such difficult repair scenarios but also highlights a domain of opportunities in developing specialist robotics for repair of high-value assets, which is a subject to growing global demand. David Alatorre, Bilal Nasser, Amir Rabani, Adam Nagy-Sochacki, Xin Dong 0011, Dragos Axinte, James Kell |
IROS | 3 |