Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments Supplemental Material

A limitation of this work is related to the visual appearance of some of the object instances in the PInNED dataset. For example, the Habitat simulator's [61] rendering can cause a deterioration in the texture quality of some objects, failing to accurately reproduce them in the environment. Moreover, instances with very small or detailed components can also exhibit a degradation in their visual fidelity when instantiated in the simulator. Consequently, as the agent moves farther from these objects, their details become less discernible. As a direct consequence, detecting small target objects is a critical challenge for navigation agents tackling the PIN task. This behavior is showcased in Sec. E, where agents tackling the PIN task in the episodes of PInNED dataset face significant challenges in successfully detecting instances of inherently small object categories. In fact, despite agents such as the modular agent with DINOv2 [51] showcase good performance on the overall PIN task, detecting small objects represents one of the main limitations of current object-driven agents, as they can only be recognized when the robot is close to them. A possible future improvement could involve designing novel exploration policies that aim to bring the robot closer to surfaces where the target might be placed while leveraging different detection criteria that take into consideration the scale of the observed objects. The introduction of the Personalized Instance-based Navigation (PIN) task and the accompanying PInNED dataset has the potential to advance the field of visual navigation and Embodied AI. The PIN task fills the limitations of the current datasets for embodied navigation by requiring agents to distinguish between multiple instances of objects from the same category, thereby enhancing their precision and robustness in real-world scenarios. This advancement can lead to more capable and reliable robotic assistants and autonomous systems, especially in household settings.