Online machine learning for calibration of autonomous earth moving vehicles

A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to:

• access, by a training system, information describing movement instructions provided to one or more earth moving vehicles (EMV);
• determine, by the training system, measures of EMV movement corresponding to the movement instructions;
• generate, by the training system, a training set of data correlating movement instructions to the measures of EMV movement;
• train, by the training system, a machine-learned model based on the generated training set of data, the machine-learned model configured to predict an EMV movement resulting from a movement instruction;
• compare, by the training system and for target movement instructions, an EMV movement predicted by the machine-learned model to an actual EMV movement corresponding to the target movement instructions;
• and
• in response to the actual EMV movement drifting out of line with the EMV movement predicted by the machine-learned model, update the machine-learned model based on the comparison of the EMV movement predicted by the machine-learned model to the actual EMV movement until the machine-learned model predicts movement of the EMV to within a threshold level of precision.

A method comprising:

• accessing, by a training system, information describing movement instructions provided to one or more earth moving vehicles (EMV) and a training set of data correlating movement instructions to measures of EMV movement;
• predicting, by a machine-learned model of the training system, an EMV movement resulting from a movement instruction, the machine-learned model trained based on the accessed training set of data;
• and
• comparing, by the training system and for target movement instructions, an EMV movement predicted by the machine-learned model to an actual EMV movement corresponding to the target movement instructions;
• and
• in response to the actual EMV movement drifting out of line with the EMV movement predicted by the machine-learned model, updating the machine-learned model based on the comparison of the EMV movement predicted by the machine-learned model to the actual EMV movement until the machine-learned model predicts movement of the EMV to within a threshold level of precision.