Abstract: The systems and methods described herein may include one or more processors configured to receive a command from a user related to a subject; access a representation space associated with the command; receive a first dataset related to the command, a second dataset related to the subject, and a third dataset which includes subjects related to the command; update the representation space based on at least one of the first, second, and third dataset; generate a goal representation based on the representation space; receive, from a plurality of sensors, a sensor data of a current environment; generate a first and a second series of steps based on the goal representation and the current environment; annotate the sensor data based on performance of the first series of steps to generate an annotated senor data; and update the second series of steps based on the annotated sensor data.

Abstract: A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.

Abstract: A computer-implemented system and method includes obtaining a plurality of tasks from a first domain. A machine learning system is trained to perform a first task. A first set of prototypes is generated. The first set of prototypes is associated with a first set of classes of the first task. The machine learning system is updated based on a first loss output. The first loss output includes a first task loss, which takes into account the first set of prototypes. The machine learning system is trained to perform a second task. A second set of prototypes is generated. The second set of prototypes is associated with a second set of classes of the second task. The machine learning system is updated based on a second loss output. The second loss output includes a second task loss, which takes into account the second set of prototypes. The machine learning system is updated based on the second loss output. The machine learning system is fine-tuned with a new task from a second domain.

Abstract: A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.