5 citations · 5 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights
Yuchen Liu, Lino Lerch, Luigi Palmieri +4
Predicting human behavior in shared environments is crucial for safe and efficient human-robot interaction. Traditional data-driven methods to that end are pre-trained on domain-sp…
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…
Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments
Andrey Rudenko, Luigi Palmieri, Johannes Doellinger +2
Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from…
THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset
Andrey Rudenko, Tomasz P. Kucner, Chittaranjan S. Swaminathan +3
Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and…