To enhance the disambiguation and object localization capabilities of the proposed system, several additional types of semantic information could be integrated:
Temporal Context: Incorporating temporal information about when objects are typically used or moved could provide valuable context. For example, knowing that a "coffee mug" is often found in the kitchen during morning hours could help the robot prioritize its search in that location at specific times of the day.
User Preferences and History: Personalization through user preferences could significantly improve the system's accuracy. By learning from past interactions, the robot could prioritize certain objects or locations based on the user's habits, such as frequently used items or preferred storage places.
Functional Attributes: Understanding the functional attributes of objects (e.g., "a bowl is used for serving food") could help the robot infer likely locations based on the context of the request. For instance, if a user asks for a "serving bowl," the robot could deduce that it is more likely to be in the dining room rather than the kitchen cabinet.
Social and Cultural Context: Integrating social and cultural knowledge could also enhance the robot's understanding. For example, knowing that certain objects are culturally significant or commonly used in specific ways could guide the robot's search strategy.
Environmental Context: Information about the environment, such as the layout of the space, common pathways, and areas of high activity, could help the robot navigate more effectively. This could include mapping out frequently accessed areas or understanding the typical arrangement of objects in a room.
By leveraging these additional types of semantic information, the robot could achieve a more nuanced understanding of its environment, leading to improved disambiguation and localization capabilities.