METHOD AND SYSTEM FOR TASK EXECUTION IN DYNAMIC HETEROGENEOUS ROBOTIC ENVIRONMENT

A method and system for task execution in dynamic heterogeneous robotic environment is disclosed. The method includes extracting data associated with a plurality of data categories and further deriving a plurality of factors from the extracted data. The method further includes determining a plurality of correlations among the plurality of factors based on the deep learning network. The method further includes deriving a plurality of sentiment parameters for a set of factors from the plurality of factors based on the plurality of correlations. The method may further includes simulating execution of the at least one current task by employing a plurality of robots. The method may further includes iteratively re-adjusting at least one of the plurality of sentiment parameters based on reinforcement learning performed on a result of the simulating. The method may further includes executing the at least one current task by employing the plurality of robots.