Papers
18
Total Citations
285
H-Index
8
About
Wolfgang Ertel is a prominent German researcher whose work spans robotics, machine learning, and artificial intelligence, with particular expertise in Learning from Demonstration (LfD) and reinforcement learning. His most influential contribution, "Robot Learning by Demonstration with local Gaussian process regression" (2010), has garnered 109 citations and helped establish principled probabilistic approaches to teaching robots complex motor skills without explicit programming. Ertel further advanced the field with practical innovations such as the LAT algorithm, which offers computationally efficient trajectory learning, and explored high-level symbolic LfD through conceptual spaces and subspace clustering, tackling the long-standing symbol grounding problem in autonomous agents. Beyond his research publications, Ertel has made a substantial impact as an educator. His German-language textbook *Grundkurs Künstliche Intelligenz* and his classroom-ready crawling robot demonstrator reflect a deep commitment to making AI and reinforcement learning accessible to students. His more recent work examines sociotechnical implications of AI, including rebound effects of automation in everyday life, and the development of proactively conversational household robots. With a career bridging theoretical rigor and applied robotics, Ertel has established himself as both a capable researcher and an influential voice in AI education and ethics.
Research Focus
Key Achievements
Top Papers
- 1Robot Learning by Demonstration with local Gaussian process regression109 citations · 2010
- 2Machine Learning and Data Mining31 citations · 2011
- 3LAT: A simple Learning from Demonstration method23 citations · 2014
- 4
- 5Reinforcement Learning18 citations · 2011
- 6Grundkurs Künstliche Intelligenz16 citations · 2009
- 7
- 8The Crawler, A Class Room Demonstrator for Reinforcement Learning14 citations · 2009
- 9KURT: A Household Assistance Robot Capable of Proactive Dialogue7 citations · 2022
- 10