Papers
18
Total Citations
268
H-Index
10
About
Andrey Rudenko is a robotics and human-robot interaction researcher whose work spans social robot navigation, human motion prediction, and the dynamics of human-robot collaboration. His research addresses a fundamental challenge in autonomous systems: enabling robots to operate safely, efficiently, and naturally alongside people in dynamic, real-world environments. Among his most influential contributions is his work on long-term human motion prediction, where his planning-based social force approach and group-aware models have helped robots anticipate pedestrian behavior more accurately in crowded spaces. These methods, garnering over 50 citations each, represent meaningful advances in autonomous navigation. He has also explored the philosophical and practical dimensions of human-robot interaction, notably examining how robots that exhibit human-like errors may actually foster more natural collaboration — a perspective that has attracted 65 citations and sparked broader discussion in the field. Rudenko's recent work expands into child-robot interaction, multimodal robot communication, and context-aware model predictive control for socially compliant navigation. His THÖR-MAGNI dataset further demonstrates his commitment to providing the research community with robust benchmarking resources. Across his career, Rudenko has consistently bridged computational rigor with human-centered design, making him a notable voice in socially intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1Errare humanum est: Erroneous robots in human-robot interaction65 citations · 2016
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- 3Human Motion Prediction Under Social Grouping Constraints30 citations · 2018
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- 6A Fast Random Walk Approach to Find Diverse Paths for Robot Navigation14 citations · 2016
- 7Efficient Context-Aware Model Predictive Control for Human-Aware Navigation13 citations · 2024
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