Alex Mitrevski

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