16 citations · 21 across the 12 of their papers we have counts for
13 papers
Conflict Mitigation in Shared Environments using Flow-Aware Multi-Agent Path Finding
Lukas Heuer, Yufei Zhu, Luigi Palmieri +4
Deploying multi-robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments…
HiCrowd: Hierarchical Crowd Flow Alignment for Dense Human Environments
Yufei Zhu, Shih-Min Yang, Martin Magnusson +1
Navigating through dense human crowds remains a significant challenge for mobile robots. A key issue is the freezing robot problem, where the robot struggles to find safe motions a…
Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets
Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko +4
Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently…
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +2
Long-term human motion prediction (LHMP) is important for the safe and efficient operation of autonomous robots and vehicles in environments shared with humans. Accurate prediction…
NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping
Yufei Zhu, Shih-Min Yang, Andrey Rudenko +3
Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encod…
FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Yiming Zhong, Yumeng Liu, Chuyang Xiao +7
Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing metho…
EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
Yufei Zhu, Yiming Zhong, Zemin Yang +4
Dexterous robotic hands often struggle to generalize effectively in complex environments due to the limitations of models trained on low-diversity data. However, the real world pre…
Fast Online Learning of CLiFF-maps in Changing Environments
Yufei Zhu, Andrey Rudenko, Luigi Palmieri +3
Maps of dynamics are effective representations of motion patterns learned from prior observations, with recent research demonstrating their ability to enhance various downstream ta…
LaCE-LHMP: Airflow Modelling-Inspired Long-Term Human Motion Prediction By Enhancing Laminar Characteristics in Human Flow
Yufei Zhu, Han Fan, Andrey Rudenko +3
Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, inc…
THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Robot Interaction
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu +7
We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social navigation: e.g., modellin…
CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +4
Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularl…
A Data-Efficient Approach for Long-Term Human Motion Prediction Using Maps of Dynamics
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +2
Human motion prediction is essential for the safe and smooth operation of mobile service robots and intelligent vehicles around people. Commonly used neural network-based approache…
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu +9
Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co…