Miscalibration detection for virtual reality and augmented reality systems

A non-transitory, computer-readable storage medium encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations including:

• accessing, as accessed sets of sensor data, sets of sensor data from one or more devices that each include a first sensor and a second sensor, each set of sensor data comprising first sensor data from the first sensor and second sensor data from the second sensor;
• generating distorted sets of sensor data by introducing distortion to the accessed sets of sensor data;
• generating, as a generated feature vector, a feature vector for each accessed set of sensor data and for each distorted set of sensor data;
• and
• training a miscalibration detection model to predict whether a miscalibration of sensors of a device has occurred by:
• processing each generated feature vector with the miscalibration detection model to obtain a corresponding output of the miscalibration detection model;
• determining a label for each generated feature vector, the label indicating whether the generated feature vector represents an accurate calibration or a miscalibration;
• comparing outputs of the miscalibration detection model with corresponding labels for the generated feature vectors;
• and
• adjusting parameters of the miscalibration detection model based on the comparing outputs of the miscalibration detection model with corresponding labels for the generated feature vectors.

A computer-implemented system comprising one or more computers and one or more non-transitory, computer-readable storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations including:

• accessing, as accessed sets of sensor data, sets of sensor data from one or more devices that each include a first sensor and a second sensor, each set of sensor data comprising first sensor data from the first sensor and second sensor data from the second sensor;
• generating distorted sets of sensor data by introducing distortion to the accessed sets of sensor data;
• generating, as a generated feature vector, a feature vector for each accessed set of sensor data and for each distorted set of sensor data;
• and
• training a miscalibration detection model to predict whether a miscalibration of sensors of a device has occurred by:
• processing each generated feature vector with the miscalibration detection model to obtain a corresponding output of the miscalibration detection model;
• determining a label for each generated feature vector, the label indicating whether the generated feature vector represents an accurate calibration or a miscalibration;
• comparing outputs of the miscalibration detection model with corresponding labels for the generated feature vectors;
• and
• adjusting parameters of the miscalibration detection model based on the comparing outputs of the miscalibration detection model with corresponding labels for the generated feature vectors.