System and method for machine-learning enabled micro-assembly control with the aid of a digital computer

A method for machine-learning enabled micro-assembly control with an aid of a digital computer, comprising:

• obtaining, by one or more processors implementing one or more machine learning algorithms, one or more parameters of a system for positioning a micro-object, the system comprising a plurality of programmable electrodes, the electrodes configured to induce a movement of the micro-objects when the micro-objects are proximate to the electrodes upon a generation of one or more electric potentials by one or more of the electrodes;
• modeling by one or more of the processors capacitance between the micro-object and the electrodes using the parameters;
• estimating by one or more of the processors a position of the micro-object based on at least one sensor measurement;
• receive by one or more of the processors further position of the micro-object;
• define by one or more of the processors using the capacitance a continuous representation of a dynamic model for movement of the micro-object due to electrode potentials generated by at least one of the plurality of the programmable electrodes, wherein the dynamic model is associated with constraints expressed as expectations;
• apply by one or more of the processors one of the machine learning algorithms to at least a portion of the continuous representation of the dynamic model to perform an optimization of electrode potentials to be generated by at least one of the plurality of the programmable electrodes to move the micro-object from the estimated position to the further position;
• and
• using the optimized electrode potentials to actuate at least one of the plurality of the programmable electrodes to induce the movement of the micro-object from the estimated position to the further position.