Abstract: Conventional robotic systems often fail to provide effective escorting services as they lack awareness of human motion dynamics. Present disclosure provides method and system for providing robot-based escorting service. The system tracks a user utilizing the robot-based escort service using a human re-identification technique and a human movement tracking technique. The human re-identification technique ensures that the same user is identified every time in crowded spaces and human movement tracking technique predicts a user state at intervals indicating whether user is following, lagging, or stopping based on re-identification performed by the human re-identification technique. Thereafter, the system adjust a speed of the robot in case it is determined that the user is either lagging or stopping, thereby enabling the robot to adapt its speed according to user's movements which further helps in providing seamless experience to user.

Abstract: The disclosure generally relates to methods and systems for complex natural language task understanding for embodied robots or agents. Conventional works on relation extraction generally find relevant triplets in a natural language phrase, but neither ground the task nor ground the arguments. The present disclosure implements a Grounded Argument and Task Extraction (GATE) technique that extracts a set of tasks and relevant arguments from the complex natural language instruction. The GATE uses an encoder-decoder neural network with nested decoding technique. The extracted tasks are mapped (grounded) to the known skill set of the robot and arguments are mapped (grounded) to objects within the environment, classifies the tokens as many times as possible which existing sequence labeling cannot do.

Abstract: The disclosure generally relates to methods and systems for complex natural language task understanding for embodied robots or agents. Conventional works on relation extraction generally find relevant triplets in a natural language phrase, but neither ground the task nor ground the arguments. The present disclosure implements a Grounded Argument and Task Extraction (GATE) technique that extracts a set of tasks and relevant arguments from the complex natural language instruction. The GATE uses an encoder-decoder neural network with nested decoding technique. The extracted tasks are mapped (grounded) to the known skill set of the robot and arguments are mapped (grounded) to objects within the environment, classifies the tokens as many times as possible which existing sequence labeling cannot do.