1. A method of determining a control signal for a robot based on a random k-nearest neighbors (RKNN) learning process, the method being performed by a computing platform having one or more processors executing instructions stored by a non-transitory computer-readable storage medium, the method comprising: receiving first input features of a first type and second input features of a second type; determining a subset of features comprising a portion of the first input features and at least one feature from the second input features, where the determining further comprises dynamically selecting a number of features in the subset based on a speed of computation and an accuracy of a prediction; comparing individual features of the subset to corresponding features of a plurality of training feature sets; based on the comparison, determining a similarity measure for a given training set of the plurality of training feature sets, the similarity measure characterizing a similarity between the individual features of the subset and features of the given training set; responsive to the similarity measure breaching a threshold, selecting one or more training sets from the plurality of training feature sets; determining one or more potential control signals for the robot, individual ones of the one or more potential control signals being associated with a corresponding training set of the plurality of training feature sets; and determining the control signal based on a RKNN transformation obtained from the one or more potential control signals; wherein the control signal is configured to cause the robot to execute an action.