Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them

Autonomous Learning Of Robust Visual Object Detection And Identification On A Humanoid

In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learned for further object identification using Cartesian Genetic Programming (CGP). The learned identification is able to provide robust and fast segmentation of the objects, without using features. We showcase our system and its performance on the iCub humanoid robot

Autonomous learning of robust visual object detection and identification on a humanoid

In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learned for further object identification using Cartesian Genetic Programming (CGP). The learned identification is able to provide robust and fast segmentation of the objects, without using features. We showcase our system and its performance on the iCub humanoid robot