Papers
29
Total Citations
930
H-Index
12
About
Richard Bormann is a robotics researcher whose work spans autonomous mobile systems, robotic manipulation, and human-robot interaction. Best known for his influential 2020 survey on learning-based robotic grasping — which has accumulated 274 citations — Bormann has helped shape the field's understanding of machine learning approaches to vision-based manipulation, a foundational challenge in modern robotics. His contributions extend significantly into autonomous service robotics, particularly professional cleaning applications. His widely cited work on room segmentation (153 citations) and indoor coverage path planning (98 citations) established essential methodological frameworks for mobile robots navigating and operating in structured indoor environments. Bormann has demonstrated a strong commitment to translating research into practical systems, evidenced by his development of autonomous robotic cleaning assistants for office environments and dirt-detection algorithms that enable context-aware cleaning behavior. His involvement in the pan-European ACCOMPANY project reflects a broader interest in socially assistive robotics, designing companion systems for elderly users with careful attention to human-centered design and proxemics. Additional contributions to warehouse automation and 3D point cloud feature description further illustrate the breadth of his expertise. Across his career, Bormann has consistently bridged fundamental research and real-world robotic deployment.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Learning-Based Robotic Grasping274 citations · 2020
- 2Room segmentation: Survey, implementation, and analysis153 citations · 2016
- 3Indoor Coverage Path Planning: Survey, Implementation, Analysis98 citations · 2018
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- 7Towards Automated Order Picking Robots for Warehouses and Retail32 citations · 2019
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- 10Autonomous dirt detection for cleaning in office environments27 citations · 2013