Kojiro Takeyama

2 accepted papers

2025

LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality

ICLR 2025poster

Understanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments. Modeling human trajectories in these spaces requires insight into how individuals maneuver around physical obstacles and manage social navigation dynamics. These dynamics include…

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2025

TR-LLM: Integrating Trajectory Data for Scene-Aware LLM-Based Human Action Prediction

Kojiro Takeyama, Yimeng Liu, Misha Sra

IROS 2025

Accurate prediction of human behavior is crucial for AI systems to effectively support real-world applications, such as autonomous robots anticipating and assisting with human tasks. Real-world scenarios frequently present challenges such as occlusions and incomplete scene observations, which can co

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