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 subtle behaviors influenced by proxemics - the social use of space, such as stepping aside to allow others to pass or choosing longer routes to avoid collisions. Previous research has developed datasets of human motion in indoor scenes, but these are often limited in scale and lack the nuanced social navigation dynamics common in home environments. To address this, we present LocoVR, a dataset of 7000+ two-person trajectories captured in virtual reality from over 130 different indoor home environments. LocoVR provides accurate trajectory and precise spatial information, along with rich examples of socially-motivated movement behaviors. For example, the dataset captures instances of individuals navigating around each other in narrow spaces, adjusting paths to respect personal boundaries in living areas, and coordinating movements in high-traffic zones like entryways and kitchens. Our evaluation shows that LocoVR significantly enhances model performance in three practical indoor tasks utilizing human trajectories, and demonstrates predicting socially-aware navigation patterns in home environments.

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 compromise predictive accuracy. Thus, traditional video-based methods often struggle due to limited temporal and spatial perspectives. Large Language Models (LLMs) offer a promising alternative. Having been trained on a large text corpus describing human behaviors, LLMs likely encode plausible sequences of human actions in a home environment. However, LLMs, trained primarily on text data, lack inherent spatial awareness and real-time environmental perception. They struggle with understanding physical constraints and spatial geometry. Therefore, to be effective in a real-world spatial scenario, we propose a multimodal prediction framework that enhances LLM-based action prediction by integrating physical constraints derived from human trajectories. Our experiments demonstrate that combining LLM predictions with trajectory data significantly improves overall prediction performance. This enhancement is particularly notable in situations where the LLM receives limited scene information, highlighting the complementary nature of linguistic knowledge and physical constraints in understanding and anticipating human behavior.Project page: https://sites.google.com/view/trllmƒusp=sharingGithub repo: https://github.com/kojirotakeyama/TR-LLM/blob/main/readme.md