Papers
23
Total Citations
725
H-Index
13
About
Daniel H. Grollman is a pioneering researcher in robot learning from demonstration (LfD), with a particular focus on enabling non-expert users to teach robots new skills through natural interaction. His key contributions center on making robot learning more accessible and robust, especially through his work on learning from both successful and failed demonstrations. Grollman’s most influential paper, "Dogged Learning for Robots" (2007, 132 citations), introduced the concept of lifelong adaptation for ubiquitous robots, allowing them to modify their behavior in response to changing environments. He further advanced the field with "Robot learning by demonstration" (2013, 97 citations) and developed methods for incrementally learning subtasks from unsegmented demonstrations (2010, 87 citations). Perhaps most notably, Grollman challenged conventional LfD paradigms with his work on learning from failed demonstrations (2011, 85 citations; 2012, 46 citations), showing that mistakes can be valuable teaching moments. His research has been instrumental in developing intuitive interfaces, including Wiimote-based control systems for lifelong robot learning (2008, 24 citations), and has been applied to domains ranging from robot soccer to remote robotic laboratories. With over 600 total citations, Grollman’s work continues to shape how robots can learn from everyday human interaction.
Research Focus
Key Achievements
Top Papers
- 1Dogged Learning for Robots132 citations · 2007
- 2Robot learning by demonstration97 citations · 2013
- 3Incremental learning of subtasks from unsegmented demonstration87 citations · 2010
- 4Donut as I do: Learning from failed demonstrations85 citations · 2011
- 5Sparse incremental learning for interactive robot control policy estimation58 citations · 2008
- 6Robot Learning from Failed Demonstrations46 citations · 2012
- 7Learning robot soccer skills from demonstration39 citations · 2007
- 8Remote Robotic Laboratories for Learning from Demonstration31 citations · 2012
- 9Wiimote interfaces for lifelong robot learning24 citations · 2008
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