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Quadruped robots are currently used in industrial robotics as mechanical aidto automate several routine tasks. However, presently, the usage of such arobot in a domestic setting is still very much a part of the research. Thispaper discusses the understanding and virtual simulation of such a robotcapable of detecting and understanding human emotions, generating its gait, andresponding via sounds and expression on a screen. To this end, we use acombination of reinforcement learning and software engineering concepts tosimulate a quadruped robot that can understand emotions, navigate throughvarious terrains and detect sound sources, and respond to emotions usingaudio-visual feedback. This paper aims to establish the framework of simulatinga quadruped robot that is emotionally intelligent and can primarily respond toaudio-visual stimuli using motor or audio response. The emotion detection fromthe speech was not as performant as ERANNs or Zeta Policy learning, stillmanaging an accuracy of 63.5%. The video emotion detection system producedresults that are almost at par with the state of the art, with an accuracy of99.66%. Due to its "on-policy" learning process, the PPO algorithm wasextremely rapid to learn, allowing the simulated dog to demonstrate aremarkably seamless gait across the different cadences and variations. Thisenabled the quadruped robot to respond to generated stimuli, allowing us toconclude that it functions as predicted and satisfies the aim of this work.

e-Inu: Simulating A Quadruped Robot With Emotional Sentience