A Self-Organized Internal Models Architecture for Coding Sensory–Motor Schemes

Abstract

Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that simulation, prediction, and multi-modal integration are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity. Research in this direction has brought extensive empirical evidence, suggesting that Internal Models are suitable mechanisms for sensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints. We propose the Self-Organized Internal Models Architecture (SOIMA), a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensorymotor schemes. Our approach addresses integrally the issues of current implementations of Internal Models. We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.

Visual Learning for Reaching and Body-Schema with Gain-Field Networks

“…Lanillos et al used them for constructing a probabilistic body map for self-perception [21]. Besides, [22] employed self-organizing maps for learning inverse-forward kinematics for self-perception whereas [23] and [24] used them for the learning of tactile maps and body image. In comparison, gain-field networks can combine advantageously the topological self-organization property of SOM with autoencoding and the nonlinear probabilistic mapping property of Bayesian networks based on multiplication.…”

Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

“…Brain's plasticity, also known as neuroplasticity, is the key to humans capability to learn and adapt their behaviour. The plastic changes happen in neural pathways as a result of the multimodal sensori-motor interaction in the environment [22]. In other words, the cortical plasticity enables the self-organization in the brain, that in turn enables the emergence of consistent representations of the world [23].…”

“…This "knowledge" was used in return to control the robot behaviour, and increase its performance in the recognition of its hand in different postures. A quite similar approach is followed Escobar-Juarez et al [22] who proposed the Self-Organized Internal Models Architecture (SOIMA) that models the CDZ framework based on internal models [40]. The necessary property of bidirectionality is pointed out by the authors.…”

“…Nevertheless, Hebb's and Oja's rules were both used in recent works with good results, respectively in References [22,42]. Hence, we applied and compared both rules.…”

A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control

“…These approaches require very large amounts of training data to properly constrain the learning algorithm, which is impractical in many situations. Other distributed implementations (based on SOM-like sensorimotor “patches,” Kohonen, 2013 ) are reported e.g., in Zahra and Navarro-Alarcon ( 2019 ), Pierris and Dahl ( 2017 ), and Escobar-Juarez et al ( 2016 ), yet, the stability properties of its algorithms are not rigorously analyzed.…”