Mark G. Pfeiffer
Papers
9
Total Citations
803
H-Index
6
About
Mark G. Pfeiffer is a leading researcher at the intersection of robotics, machine learning, and autonomous navigation. His work focuses on developing data-driven motion planning systems that enable robots to navigate complex, dynamic environments—from crowded city streets to high-speed racetracks. Pfeiffer’s major contributions include pioneering end-to-end learning approaches for mapless navigation, where raw sensor data is directly mapped to control commands, as demonstrated in his highly cited 2017 paper (426 citations). He is also known for advancing sample-efficient deep reinforcement learning through reinforced imitation, combining expert demonstrations with RL to train robust navigation policies (198 citations). A key theme in his research is socially-compliant robot behavior; his work on cooperative partial motion planning uses maximum entropy models to predict and act predictably around humans (85 citations). Notably, Pfeiffer contributed to the AMZ Driverless project, developing the full autonomous racing system that won the 2019 Formula Student Driverless competition. His research has accumulated over 800 citations, establishing him as a key figure in autonomous ground robotics and human-aware navigation.
Research Focus
Key Achievements
Top Papers
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- 6AMZ Driverless: The full autonomous racing system11 citations · 2020
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