Andre L. Luvizotto

TL;DR: This research identifies the requirements for cooperation, and presents a cognitive system that implements these requirements, and demonstrates the system's ability to allow a Nao humanoid robot to learn a nontrivial cooperative task in real-time.

TL;DR: This chapter presents Distributed Adaptive Control in a concise form and shows how it is allowing to extend the different subsystems to more biophysical detailed models, which will allow to better understand the biological systems, but moreover advance DACs behavioral capabilities and generality.

TL;DR: A real time implementation of TPC for classifying faces, a complex natural stimuli that mammals are constantly confronted with, is proposed and it is shown that the TPC-based model can recognize faces with a correct ratio of 97 % without any face-specific strategy.

TL;DR: This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control into one general autonomous robot controller.

TL;DR: The solution to the TPC decoding problem proposed here suggests that cortical processing streams might well consist of sequential operations where spatio-temporal transformations at lower levels forming a compact stimulus encoding using TPC that are subsequently decoded back to a spatial representation using wavelet transforms.