Papers
3
Total Citations
8
H-Index
2
About
Marc Patrick Zapf’s research lies at the critical intersection of robotics, autonomous systems, and human-aware perception. His work focuses on enhancing the safety and efficiency of mobile robots operating in dynamic, human-populated environments. A key contribution is his development of systematic methods for **sensor visibility estimation**, a concept vital for safety-critical applications in automotive and robotics. By defining where a sensor can measure or is blind, his 2022 work provides metrics to improve functional safety, a foundational step for trustworthy autonomous navigation. Zapf also advances **predictive human modeling**, as demonstrated in his 2019 paper on pedestrian density prediction. By using geometric cost maps and semantic room categorization, his method allows robots to anticipate human presence in unexplored areas, enabling more efficient and cautious exploration. Further, his 2018 research introduces a framework for classifying **person-object interactions** from RGB-D data, clustering semantic contexts to help robots understand complex social and physical environments. While his citation counts (2–3 per paper) reflect an early-career stage, the foundational nature of his contributions—addressing visibility, prediction, and interaction understanding—positions his work as a building block for next-generation, human-aware autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Pedestrian Density Prediction for Efficient Mobile Robot Exploration3 citations · 2019
- 3