Jan Docekal
Papers
2
Total Citations
28
H-Index
2
About
Jan Docekal is a researcher at the forefront of human-robot interaction (HRI), with a specialized focus on the technical challenges of close-proximity collaboration. His primary research area centers on computer vision, specifically the robust detection and tracking of human body keypoints when robots and humans work in tight, shared spaces. Docekal's major contribution lies in critically evaluating the performance of state-of-the-art human keypoint detectors under these unique, constrained conditions—where only partial body views (like hands and torso) are available. His most cited work, "Human Keypoint Detection for Close Proximity Human-Robot Interaction" (2022), has garnered 26 citations, establishing a foundational benchmark for the field. This research is vital for ensuring safe and intuitive robot behavior, as accurate perception is the first step toward responsive interaction. By surveying existing datasets and methodologies, Docekal has identified critical gaps in current detection systems, paving the way for more reliable and context-aware HRI. His work is essential reading for any student or researcher developing robots that must operate safely and effectively in direct contact with humans.
Research Focus
Key Achievements
Top Papers
- 1Human Keypoint Detection for Close Proximity Human-Robot Interaction26 citations · 2022
- 2Human keypoint detection for close proximity human-robot interaction2 citations · 2022