CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Deep robot sketching: an application of deep Q-learning networks for human-like sketching
Authors
Carlos Balaguer
Raul Fernandez Fernandez
Juan G. Victores
Publication date
1 September 2023
Publisher
Elsevier
Doi
Cite
Abstract
© 2023 The Authors. Published by Elsevier B.V. This research has been financed by ALMA, ‘‘Human Centric Algebraic Machine Learning’’, H2020 RIA under EU grant agreement 952091; ROBOASSET, ‘‘Sistemas robóticos inteligentes de diagnóstico y rehabilitación de terapias de miembro superior’’, PID2020-113508RBI00, financed by AEI/10.13039/501100011033; ‘‘RoboCity2030-DIHCM, Madrid Robotics Digital Innovation Hub’’, S2018/NMT-4331, financed by ‘‘Programas de Actividades I+D en la Comunidad de Madrid’’; ‘‘iREHAB: AI-powered Robotic Personalized Rehabilitation’’, ISCIIIAES-2022/003041 financed by ISCIII and UE; and EU structural fundsThe current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs.Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEUnión Europea. H2020Ministerio de Ciencia e Innovación (MICINN)/ AEI/10.13039/501100011033;Comunidad de MadridInstituto de Salud Carlos III (ISCIII)/UEROBOTICSLABpu
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Docta Complutense
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:docta.ucm.es:20.500.14352/...
Last time updated on 31/08/2023