RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization

TL;DR

RoboCoDraw is a real-time robotic drawing system that uses GAN-based style transfer to create stylized avatars from human faces and employs path optimization for efficient robotic drawing.

Contribution

The paper introduces Avatar-GAN for improved face-to-avatar style transfer and a novel RKGA-based path optimization for faster robotic drawing.

Findings

Successfully generates stylized avatars with high likeness.

Achieves efficient robotic drawing paths with reduced execution time.

Demonstrates real-time interactive drawing with a collaborative robot.

Abstract

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the Avatar-GAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.