Alex Mitrevski

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

Forschungsgebiet

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

Porträt Aleksandar Mitrevski, wissenschaftlicher Mitarbeiter Informatik
E-Mail: 
aleksandar.mitrevski [at] h-brs.de

Sankt Augustin

Grantham-Allee 20
53757
Sankt Augustin
Raum: 
C201
Telefon: 
+492241 865206

Profil

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

Publikationen