| CPC G06N 20/00 (2019.01) | 19 Claims |
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1. A computer-implemented method comprising:
obtaining input data representative of a state of a robot system relative to an environment, the robot system comprising a plurality of types of sensors, wherein the input data comprises a temporal sequence of sensor data for each of a plurality of different channels of sensor data reporting properties sensed by respective types of sensors;
forming a set of training records from the input data, wherein each training record includes temporally matched sensor data selected from across the plurality of different channels of sensor data, wherein each type of sensor outputs a corresponding one of the different channels of sensor data;
training a first model to encode inputs corresponding to the plurality of different channels of sensor data as vectors in an embedding space with self-supervised learning based on the set of training records, wherein the training comprises iteratively adjusting parameters of the first model based on outputs of an objective function, wherein the objective function causes the parameters of the first model to be adjusted in directions that cause vectors in the embedding space to encode temporal consistency of properties sensed by the respective types of sensors in the set of training records;
causing the robot system to attempt to perform a task using the first model; and
updating the first model based on the performance of the robot system on the attempt to perform the task using the first model.
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