5. The auditory RRC-humanoid robot of claim 1 , wherein the set of linear pickup microphones comprises a right-side linear pickup microphone positioned on the right of an electronic enclosure and a left-side linear pickup microphone positioned on the left of the electronic enclosure.
1. An auditory relational robotic controller (RRC)-humanoid robot comprising: a human-like mechanical robotic system comprising a human-like tactile recording monitor, a human-like visual recording monitor, and a human-like robotic body comprising a set of bipedal limbs, a set of arms, a set of hands, a set of fingers, an energy power source, and sets of motors and gears used to move the body, limbs, arms, hands, and fingers; an auditory human-like recording monitor sensitive to an auditory frequency range of 1-20,000 cycles per second (cps), said auditory human-like recording monitor comprising a set of linear pickup microphones and a set of spectrum analyzers that convert incoming sound into electronic phonetic (a-f-t)-signal output characterized by an amplitude, frequency, time (a-f-t) chart showing the amplitude and frequency of the incoming sound as a function of time; a relational robotic controller (RRC) that satisfies a set of specification requirements for relational robotic controllers; a verbal-phoneme sound generator that generates sequences of phoneme sounds that are controlled by the RRC; an interface circuit positioned between the auditory human-like recording monitor and the RRC, said interface circuit configured to decompose an electronic phonetic (a-f-t)-signal output of each spectrum analyzer into collective modalities tuned to the characteristics of verbal speech, generate a q-magnitude and p-direction p-phoneme vector that is a suitable input to a multi-dimensional function space Nodal Map Module (NMM), standardize the p-phoneme vector to operate in a Task Selector Module (TSM), the NMM, a Sequence Stepper Module (SSM) of the RRC, and successfully activate the verbal-phoneme sound generator, and develop a speech processing methodology for obtaining a one-to-one mapping of the acoustic signals onto a phonetic structure free of segmentation errors; and a programming methodology defined by a Declarative Hierarchical Task Diagram (DHTD) specification that provides the robot a human-like, high IQ form of verbal artificial intelligence (AI).