EDBT 2026 Demo / reviewers in the wild / expert
Innes Vanderniepen
dblp:02/1749
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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 · 77% Motion planning and robot control · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation › force sensing
force estimation |
0.1 | 1 | 2011 | Estimating robot end-effector force from noisy actuator torque measurements · ICRA 2011 |
Robotics › Motion planning and robot control › robot control
admittance control |
0.0 | 1 | 2011 | Estimating robot end-effector force from noisy actuator torque measurements · ICRA 2011 |
Methods — techniques the papers use, named apart from their topics
recursive least-squares estimation · 0.1generalized-momentum disturbance observer · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Estimating robot end-effector force from noisy actuator torque measurementsabstractThis paper discusses two ways to estimate the interaction force at the end-effector of a robot. The first approach that is presented combines filtered dynamic equations with a recursive least squares estimation algorithm to provide a smoothened force signal, which is useful in the (common) case of noisy torque measurements. The second approach, which uses a generalized momentum based disturbance observer, is mainly discussed to compare it to the first approach. Although very different in appearance, it is shown that a close connection exists between both approaches. Simulation results for both algorithms are shown, and experimental results derived from a sensorless admittance controller that was implemented using the algorithms are presented. Michaël Van Damme, Pieter Beyl, Bram Vanderborght, Victor Grosu, Ronald Van Ham, Innes Vanderniepen, Arnout Matthys, Dirk Lefeber |
ICRA | 6 |