EDBT 2026 Demo / reviewers in the wild / expert
Marek Musial
dblp:08/3908
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3Systems, architecture and hardware · 2Computer networks · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 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.
| Artificial intelligence
2 papers |
Reinforcement learning · 60% Legged, aerial and field robots · 20% Robot navigation and mapping · 20% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Electronic design automation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › function approximation
state aggregation |
0.2 | 1 | 2013 | Construction of approximation spaces for reinforcement learning · J. Mach. Learn. Res. 2013 |
Robotics › Legged, aerial and field robots › aerial robots
autonomous helicopter |
0.1 | 1 | 2005 | Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005 |
Robotics › Robot navigation and mapping › fault detection
sensor fault detection |
0.1 | 1 | 2005 | Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005 |
Electronic design automation › hardware verification and test
fault diagnosis |
0.0 | 1 | 2005 | Detection of Sensor Faults in Autonomous Helicopters · ICRA 2005 |
Methods — techniques the papers use, named apart from their topics
approximation space construction · 0.2observer-based detection · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Construction of approximation spaces for reinforcement learning
Wendelin Böhmer, Steffen Grünewälder, Marek Musial, Klaus Obermayer |
J. Mach. Learn. Res. | 4 |
| 2005 | Detection of Sensor Faults in Autonomous HelicoptersabstractThis paper presents a sensor fault detection and diagnosis system for autonomous helicopters. The system has been tested with the MARVIN autonomous helicopter. Fault detection is accomplished by evaluating any significant change in the behaviour of the vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The effectiveness of the proposed approach is demonstrated by means of MARVIN experimental results. Guillermo Heredia, Aníbal Ollero, Rajesh Mahtani, Manuel Béjar, Volker Remuß, Marek Musial |
ICRA | 6 |
| 2004 | Mechanical model and control of an autonomous small size helicopter with a stiff main rotorabstractThe mathematical description of the kinematics and dynamics of a model helicopter is presented. Using the derived closed form solution for simplified dynamics, a controller for an autonomous model helicopter is designed. This controller is composed of two loops: the inner loop for controlling the orientation of the main rotor plain and the outer loop to control the mission parameters like coordinates of some reference point, orientation or cruise velocity. The performance of the proposed controller is demonstrated for the case of position control in a computer simulation. Konstantin Kondak, Carsten Deeg, Günter Hommel, Marek Musial, Volker Remuß |
IROS | 4 |
| 1997 | On-Line Timed Protocol Trace Analysis Based on Uncertain State Descriptions
Marek Musial |
FORTE | 1 |