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 the 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 some of the 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 some of the electrodes to move the micro-object from the estimated position to the further position;
• using the optimized electrode potentials to actuate at least some of the electrodes to induce the movement of the micro-object from the estimated position to the further position.