Alex Mitrevski

Doktorand, Wissenschaftlicher Mitarbeiter, Team Leader b-it-bots@Home

Autonomous Systems

Promotionsthema: Skill Generalisation and Experience Acquisition for Predicting and Avoiding Execution Failures betreut durch Prof. Dr. Gerhard Lakemeyer and Prof. Dr. Paul G. Plöger

Kontakt

E-Mail:

aleksandar.mitrevski [at] h-brs.de

Grantham-Allee 20

53757

Sankt Augustin

Raum:

C201

Telefon:

Fax:

Forschungsgebiete

Knowledge representation and reasoning (knowledge retrieval, forgetting mechanisms, template- and case-based reasoning)
Lifelong robot learning
Simulation-based robot learning and reasoning
Robot fault detection and diagnosis
Cognitive robotics

Lehre

SS 2021
- TA Mathematics for Robotics and Control
- Project coach Software Development Project
WS 2020
- TA Mathematics for Robotics and Control
- Project coach Software Development Project
SS 2020
- TA Mathematics for Robotics and Control
- Project coach Software Development Project
WS 2019
- LB Research and Development Colloquium
- TA Mathematics for Robotics and Control
SS 2019
- LB Fault Detection and Diagnosis
WS 2018
- LB Mathematics for Robotics and Control
- TA Scientific Experimentation and Evaluation
SS 2018
- LB Research and Development Colloquium (mit Argentina Ortega)
- TA Mathematics for Robotics and Control
- TA Scientific Experimentation and Evaluation
WS 2017/18
- TA Mathematics for Robotics and Control
- TA Scientific Experimentation and Evaluation
SS 2017
- TA Probabilistic Methods for Robotics
- TA Mathematics for Robotics and Control (mit Santosh Thoduka)
- TA Scientific Experimentation and Evaluation (mit Santosh Thoduka)

Betreute Masterarbeiten

Visuomotor policy learning for predictive manipulation
Robust environment sound classification and anomaly detection using deep learning
Towards improvements on RoboCup@Home robots architecture, capabilities and development process

Betreute R&D-Projekte

Incorporating contextual knowledge into human-robot collaborative task execution
Learning corrective models for multistep actions by analysing videos
Registering and visualizing point cloud data with existing 3D CityGML Models
A comparative analysis of fault detection approaches in mobile robots
Tell your robot what to do: Evaluation of natural language models for robot command processing
Manipulating Handles in Domestic Environments
Learning grasp evaluation models using synthetic 3D object-grasp representations
Dynamic motion primitives
Ontology-Based Robot Fault Diagnosis
Automated Test Generation for Robot Self-Examination
Semantic information by acoustic clues: A modern approach to anomaly detection for robotics