Scholar
Andrey Rudenko
Google Scholar ID: BlSfMYwAAAAJ
Munich Institute of Robotics and Machine Intelligence, Technical University of Munich
Robotics
Human Motion Prediction
Path and Motion Planning
Human-Robot Interaction
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Homepage
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Citations & Impact
All-time
Citations
1,589
H-index
12
i10-index
14
Publications
20
Co-authors
19
list available
Contact
Email
andrey.rudenko@de.bosch.com
GitHub
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Publications
11 items
Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding
2026
Cited
0
Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping
2025
Cited
0
Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets
2025
Cited
0
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
2025
Cited
0
Gaze-supported Large Language Model Framework for Bi-directional Human-Robot Interaction
2025
Cited
0
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
2025
Cited
0
Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization
2025
Cited
0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights
2025
Cited
0
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Resume (English only)
Academic Achievements
Published multiple papers on human motion prediction, including in IEEE RA-L (THÖR and urban occupancy priors)
Co-organized the 'Long-term Human Motion Prediction' workshops at ICRA 2020, 2021, and 2022
Co-organized the 'Benchmarking Trajectory Forecasting Models' workshop at ECCV 2020
Contributed to the development of the Atlas Benchmark for motion prediction algorithms
Authored a comprehensive survey reviewing 170+ human motion prediction methods, benchmarks, and open challenges
THÖR paper accepted to IEEE RA-L and presented at ICRA 2020
First public presentation of Atlas Benchmark at RSS 2021 Workshop on Social Navigation
Research Experience
Joined Robert Bosch as a full-time employee in March 2021
Participated in the DARKO project with an integration week at Örebro University
Developed THÖR dataset: a novel dataset of accurate and diverse human motion trajectories with eye gaze data
Built an MDP-based socially-aware motion predictor
Learned occupancy priors in urban environments using Inverse Reinforcement Learning and CNNs from semantic maps
Co-authors
19 total
Luigi Palmieri
Robert Bosch GmbH Corporate Research
Kai O. Arras
Professor of Autonomous Systems
Achim J. Lilienthal
Full Professor & MIRMI Deputy Director at TU Munich / Guest Professor at Örebro University
Martin Magnusson
Professor in computer science (robotics), Örebro University
Tomasz Piotr Kucner
Assistant Professor, Aalto Univeristy
Tim Schreiter
PhD. student at TU munich
Yufei Zhu
Örebro University
Co-author 8
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