Method and system for training a machine learning model for classification of components in a material stream

A system for training a machine learning model which is configured to perform characterization of components in a heterogeneous material stream with a plurality of unknown components, the system including a processor, a computer readable storage medium, a sensory system, an analyzing unit and a separator unit, wherein the computer readable storage medium has instructions stored which, when executed by the processor, result in the processor performing operations comprising:

• operating the sensory system to scan the material stream such as to perform imaging of the material stream with the plurality of unknown components;
• predicting one or more labels and associated label probabilities for each of the unknown components in the material stream by a machine learning model which is configured to receive as input the imaging of the material stream and/or one or more features of the unknown components extracted from the imaging of the material stream;
• determining a training reward associated with each unknown component within the plurality of unknown components in the material stream;
• determining a value associated with each unknown component that is indicative of a difficulty for performing physical isolation of the unknown component from the material stream by-the separator unit;
• selecting a top number of unknown components from the plurality of unknown components in the material stream based on the training reward associated with the unknown components;
• selecting at least one unknown component from the top number of unknown components based at least partially on the value indicative of the difficulty for performing physical isolation;
• operating the separator unit for physically isolating the selected at least one unknown component from the material stream, wherein the separator unit is configured to move the selected at least one unknown component to a separate accessible compartment;
• receiving for the isolated at least one unknown component a ground truth label determined by the analyzing unit automatically performing an characterization of the isolated at least one unknown component within the separate accessible compartment, wherein the determined ground truth label of the isolated at least one unknown component is added to a training database;
• and
• training an incremental version of the machine learning model using the determined ground truth label of the physically isolated at least one unknown component;
• wherein the analyzing unit is configured to perform the characterization by subjecting the isolated at least one unknown component to an automated chemical analysis for determining the ground truth label at least partially based thereon.