Title

Developing of effective mechanisms for robot perception using motivated learning and
self-organizing associative memory

Keywords

autonomous robots, neural networks, knowledge modeling, sensory data processing,
object recognition, memory modeling, artificial associative systems, motivated learning

About project

The main objective of the proposed research is to develop new effective perceptual mechanisms using generalized
idea of motivated learning (ML), and new mechanisms for associative learning and inference. In order to
use perception to acquire knowledge by interacting with the environment, it is planned to refine the associative
object recognition and scene representation system, supported by activities of a robot. It is proposed to build and
test an innovative visual and acoustic perception system for a robot, based on the mechanisms of episodic
memory. Our hypothesis is that the perception of visual and audio stimuli will bring the best results after applying
the learning systems with memory, capable of gathering and modeling of knowledge and creating of associative
memory for arbitrary spatio-temporal patterns. So, one of the challenges of this project is to base perceptual
mechanisms of a motivated agent on visual saccades, associative mechanisms, and integrated associative
memory model. An additional goal of the project is to create new mechanisms of self-organized semantic
memory using attention focus and attention switching, which, in cooperation with the episodic memory will
yield contextual representation of a robot’s actions in its environment. Semantic and episodic memory consolidation
is required in order to extend and generalize ML methods. Therefore, new inference mechanisms based on
the associative memory model will be proposed. Our hypothesis is that using the associative semantic memory
and sequential episodic memory, autonomous robot can recognize objects, scenes, and predict the outcome of its
actions. Completing these objectives will enhance the capacity of robots for operation in a complex environment.

Project is funded by Polish National  Centre of Science (NCN) under the contract UMO-2016/21/B/ST7/02220 in years 2017-2020.
Project is performed by University of Information Technology and Management in Rzeszow, Poland

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