A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories; high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications

Learning dynamic graffiti strokes with a compliant robot

We present an approach to generate rapid and fluid drawing movements on a compliant Baxter robot, by taking advantage of the kinematic redundancy and torque control capabilities of the robot. We concentrate on the task of reproducing graffiti-stylised letter-forms with a marker. For this purpose, we exploit a compact lognormal-stroke based representation of movement to generate natural drawing trajectories. An Expectation-Maximisation (EM) algorithm is used to iteratively improve tracking performance with low gain feedback control. The resulting system captures the aesthetic and dynamic features of the style under investigation and permits its reproduction with a compliant controller that is safe for users surrounding the robot

Visual-based decision for iterative quality enhancement in robot drawing.

Kwok, Ka Wai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 113-116).Abstracts in English and Chinese.ABSTRACT --- p.iChapter 1. --- INTRODUCTION --- p.1Chapter 1.1 --- Artistic robot in western art --- p.1Chapter 1.2 --- Chinese calligraphy robot --- p.2Chapter 1.3 --- Our robot drawing system --- p.3Chapter 1.4 --- Thesis outline --- p.3Chapter 2. --- ROBOT DRAWING SYSTEM --- p.5Chapter 2.1 --- Robot drawing manipulation --- p.5Chapter 2.2 --- Input modes --- p.6Chapter 2.3 --- Visual-feedback system --- p.8Chapter 2.4 --- Footprint study setup --- p.8Chapter 2.5 --- Chapter summary --- p.10Chapter 3. --- LINE STROKE EXTRACTION AND ORDER ASSIGNMENT --- p.11Chapter 3.1 --- Skeleton-based line trajectory generation --- p.12Chapter 3.2 --- Line stroke vectorization --- p.15Chapter 3.3 --- Skeleton tangential slope evaluation using MIC --- p.16Chapter 3.4 --- Skeleton-based vectorization using Bezier curve interpolation --- p.21Chapter 3.5 --- Line stroke extraction --- p.25Chapter 3.6 --- Line stroke order assignment --- p.30Chapter 3.7 --- Chapter summary --- p.33Chapter 4. --- PROJECTIVE RECTIFICATION AND VISION-BASED CORRECTION --- p.34Chapter 4.1 --- Projective rectification --- p.34Chapter 4.2 --- Homography transformation by selected correspondences --- p.35Chapter 4.3 --- Homography transformation using GA --- p.39Chapter 4.4 --- Visual-based iterative correction example --- p.45Chapter 4.5 --- Chapter summary --- p.49Chapter 5. --- ITERATIVE ENHANCEMENT ON OFFSET EFFECT AND BRUSH THICKNESS --- p.52Chapter 5.1 --- Offset painting effect by Chinese brush pen --- p.52Chapter 5.2 --- Iterative robot drawing process --- p.53Chapter 5.3 --- Iterative line drawing experimental results --- p.56Chapter 5.4 --- Chapter summary --- p.67Chapter 6. --- GA-BASED BRUSH STROKE GENERATION --- p.68Chapter 6.1 --- Brush trajectory representation --- p.69Chapter 6.2 --- Brush stroke modeling --- p.70Chapter 6.3 --- Stroke simulation using GA --- p.72Chapter 6.4 --- Evolutionary computing results --- p.77Chapter 6.5 --- Chapter summary --- p.95Chapter 7. --- BRUSH STROKE FOOTPRINT CHARACTERIZATION --- p.96Chapter 7.1 --- Footprint video capturing --- p.97Chapter 7.2 --- Footprint image property --- p.98Chapter 7.3 --- Experimental results --- p.102Chapter 7.4 --- Chapter summary --- p.109Chapter 8. --- CONCLUSIONS AND FUTURE WORKS --- p.111BIBLIOGRAPHY --- p.11

ディープラーニングを用いた視覚運動学習による適応的な描画行為

早大学位記番号:新7963早稲田大

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books

Play Among Books

How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books