Tim Goller, M.Sc.
Tim Goller, M.Sc.
Department of Electrical-Electronic-Communication Engineering
Chair of Automatic Control (Prof. Dr.-Ing. Graichen)
Cauerstr. 7
91058 Erlangen
- Phone number: +49 9131 85-61038
- Fax number: +49 9131 85-28715
- Email: tim.goller@fau.de
Research Interests
- Model predictive control of robot arms
- Manipulation planning for industrial robots
- Programming by demonstration
- Machine learning methods in robotics
Biography
- since August 2020: Research associate, Chair of Automatic Control, FAU Erlangen-Nürnberg
- 2017-2020: Master of Science in Mechatronics, FAU Erlangen-Nürnberg
- 2013-2017: Bachelor of Engineering in Mechatronics, Reutlingen University
Publications
2024
- Goller, T., Brohm, D., Völz, A., & Graichen, K. (2024). DMP-based path planning for model predictive interaction control. In Proceedings of the European Control Conference (pp. 128-133). Stockholm (Sweden).
2022
- Goller, T., Gold, T., Völz, A., & Graichen, K. (2022). Model predictive interaction control based on a path-following formulation. In Proceedings IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 551-556). Guilin (China).
2020
- Gold, T., Lomakin, A., Goller, T., Völz, A., & Graichen, K. (2020). Towards a Generic Manipulation Framework for Robots based on Model Predictive Interaction Control. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 401 - 407). Beijing, CN.
Open theses
- Learning from demonstration of robotic manipulation tasks
Current and completed theses
- Trajektorienplanung mittels DMPs innerhalb des Task-Skill-MP Frameworks
- A Concept for Fault Detection and Handling in Hierarchical Task Control of Robots with Model Predictive Interaction Control
- Online optimization of MP-specific parameters for interaction tasks
- Development of a Robot Learning Showcase for Educational Purposes
- Predicting contact situations with an eye-in-hand visual servoing system for robotics applications
- Camera-Based Gesture Control of a Robotic System with Anthropomorphic Hand
- Variable Impedance Control for Collaborative Robots
- Anbindung des Beckhoff XPlanar-Systems an das Robot Operating System
- Experimentelle Umsetzung eines kooperativen Lasttransports am XPlanar mittels DMPC
- Trajektorienplanung mit Dynamical Movement Primitives (DMP) für eine modellprädiktive Folgeregelung
- Automatisches Erfassen und Greifen bewegter Objekte
- Modellierung von Roboterbewegungen basierend auf Mehrfachdemonstrationen
- Lernen komplexer Roboterbewegungen durch Imitation mittels Dynamic Movement Primitives
- Reinforcement Learning für industrielle Transportprozesse am Beispiel des XPlanar-Systems