Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities.

While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.

To address these limitations, we propose a new task denominated Personalized Instance-based Navigation (PIN), in which an embodied agent is tasked with locating and reaching a specific personal object by distinguishing it among multiple instances of the same category.

Table 1: Comparison of key features between prior works and this work. Method Description Model Consider Evolution in Criteria Agnostic Interaction Parameter Dynamics Acc.

In this paper, we formalize a novel reinforcement learning model which explicitly represents the information structure.