Reinforcement Learning for Generative Art

Reinforcement learning (RL) is an efficient class of sequential decision-making algorithms that have achieved remarkable success in a broad range of applications, such as robotic manipulations, strategic games, or autonomous driving. The most well-known example of reinforcement learning is AlphaGo, a computer program that plays the board game Go and outperforms top human Go players. Unlike other two major machine learning categories, supervised learning and unsupervised learning, in which media artists are actively engaged, reinforcement learning has yet to result in many creative applications. Generative art is usually driven, in whole or in part, by autonomous systems that are derived from a set of rules. Interestingly, an RL policy can be seen as an autonomous system where the rules are learned by interacting with its environment. Regardless of its initial purpose, reinforcement learning has the potential to expand the boundary of generative art. However, a formal process of applying reinforcement learning to generative art does not yet exist and the current RL tools require an in-depth understanding of RL concepts. To bridge the gap, the first part of the dissertation introduces a conceptual framework to adapt reinforcement learning for generative art. The framework proposes a term RL-based generative art to denote a novel form of generative art of which the use of RL agents is the key element. The creative process of RL-based generative art and possible emergent behaviors are discussed in the framework. This leads to a discussion of several author's related practices on generative art, deep-learning art, and reinforcement learning. Those practices are critical for understanding the conceptual and technical details of each component in order to construct the framework. The second part introduces RL5, a JavaScript library for rapidly prototyping RL environments and training RL policies in web browsers. The library combines RL algorithms and RL environments into one framework and is fully compatible with p5.js. RL5 is developed with a particular focus on simplicity to favor (re)usability of RL algorithms and development of RL environments. Specifically, the library implemented three RL algorithms, Tabular Q-learning, REINFORCE, and DDPG, to cover all the three families of model-free RL, and nine RL environments that six of them address autonomous agents in steering behaviors, which can be used as building blocks for complex systems. Finally, the author demonstrates four different use cases of how to apply RL5 for pedagogical and creative applications

Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces

This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Interfaces for human-centered production and use of computer graphics assets

L'abstract è presente nell'allegato / the abstract is in the attachmen

Acquisition and reconstruction of 3D objects for robotic machining

With the evolution of the techniques of acquisition of Three-Dimensional (3D) image it became possible to apply these in more and more areas, as well as to be used for research and hobbyists due to the appearance of low cost 3D scanners. Among the application of 3D acquisitions is the reconstruction of objects, which allows for example to redo or remodel an existing object that is no longer on the market. Another rise tech is industrial robot, that is highly present in the industry and can perform several tasks, even machining activities, and can be applied in more than one type of operation. The purpose of this work is to acquire a 3D scene with low-cost scanners and use this acquisition to create the tool path for roughing a workpiece, using an industrial robot for this machining task. For the acquisition, the Skanect software was used, which had satisfactory results for the work, and the exported file of the acquisition was worked on the MeshLab and Meshmixer software, which were used to obtain only the interest part for the milling process. With the defined work object, it was applied in Computer Aided Manufacturing (CAM) software, Fusion 360, to generate the tool path for thinning in G-code, which was converted by the RoboDK software to robot code, and this also allowed to make simulation of the machining with the desired robot. With the simulation taking place as expected, it was implemented in practice, performing the 3D acquisition machining, thus being able to verify the machining technique used. Furthermore, with the results of acquire, generation of toolpath and machining, was possible to validate the proposed solution and reach a conclusion of possible improvements for this project.Com a evolução das técnicas de aquisição de imagem 3D tornou-se possível aplicá-las em cada vez mais áreas, bem como serem utilizadas por pesquisadores e amadores devido ao surgimento de scanners 3D de baixo custo. Entre as aplicações de aquisições 3D está a reconstrução de objetos, o que permite, por exemplo, refazer ou remodelar um objeto existente que não está mais no mercado. Outra tecnologia em ascensão é o robô industrial, que está muito presente na indústria e pode realizar diversas tarefas, até mesmo atividades de fabrico, e ser aplicado em mais de um tipo de operação. O objetivo deste trabalho é adquirir uma cena 3D com scanners de baixo custo e utilizar esta aquisição para criar o caminho da ferramenta para o desbaste de uma peça, utilizando um robô industrial nesta tarefa de usinagem. Para a aquisição foi utilizado o software Skanect, que obteve resultados satisfatórios para o trabalho, e o arquivo exportado da aquisição foi trabalhado nos softwares MeshLab e Meshmixer, os quais foram utilizados para obter apenas a parte de interesse para o processo de fresagem. Com o objeto de trabalho defino, este foi aplicado em software CAM, Fusion 360, para gerar o caminho de ferramentas para o desbaste em G-code, o qual foi convertido pelo Software RoboDK para código de rôbo, e este também permitiu fazer simulação da maquinação com o rôbo pretendido. Com a simulação ocorrendo de acordo com o esperado, esta foi implementada em prática, realizando a maquinação da aquisição 3D, assim podendo verificar a técnica de maquinação utilizada. Além disso com os resultados de aquisição, geração de toolpath e maquinação, foi possível validar a solução proposta e chegar a uma conclusão de possíveis melhorias para este projeto

Fabricate 2020

Fabricate 2020 is the fourth title in the FABRICATE series on the theme of digital fabrication and published in conjunction with a triennial conference (London, April 2020). The book features cutting-edge built projects and work-in-progress from both academia and practice. It brings together pioneers in design and making from across the fields of architecture, construction, engineering, manufacturing, materials technology and computation. Fabricate 2020 includes 32 illustrated articles punctuated by four conversations between world-leading experts from design to engineering, discussing themes such as drawing-to-production, behavioural composites, robotic assembly, and digital craft