GEOMETRICALLY GROUNDED LARGE LANGUAGE MODELS FOR ZERO-SHOT HUMAN ACTIVITY FORECASTING IN HUMAN-AWARE TASK PLANNING

    公开(公告)号:US20250094731A1

    公开(公告)日:2025-03-20

    申请号:US18614200

    申请日:2024-03-22

    Abstract: A method includes receiving data from one or more sensors, detecting, from the received data, one or more activities of a person in a space, converting the detected one or more activities into a natural language narration; extracting one or more items from a map corresponding to the space; determining a relevancy score for each of the extracted one or more items based on an output of a language model that receives as input a combination of the narration with a binding sequence; correlating the extracted one or more items with one or more locations on the map based on the determined relevancy for each of the extracted one or more items; and outputting a control signal for controlling a movement of one more devices based on the correlation of the extracted one or more items with the one or more locations on the map.

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