Sim-to-Real Brush Manipulation using Behavior Cloning and Reinforcement Learning

TL;DR

This paper presents a method combining behavior cloning and reinforcement learning to train a brush manipulation agent that effectively transfers skills from simulation to real-world robotic applications.

Contribution

The study introduces a novel framework that bridges the sim-to-real gap for brush manipulation using combined learning techniques and real-world robotic setup.

Findings

Agent successfully transfers policies from simulation to real-world robot.

Effective handling of high-dimensional continuous action spaces.

Demonstrated applicability in real painting tasks.

Abstract

Developing proficient brush manipulation capabilities in real-world scenarios is a complex and challenging endeavor, with wide-ranging applications in fields such as art, robotics, and digital design. In this study, we introduce an approach designed to bridge the gap between simulated environments and real-world brush manipulation. Our framework leverages behavior cloning and reinforcement learning to train a painting agent, seamlessly integrating it into both virtual and real-world environments. Additionally, we employ a real painting environment featuring a robotic arm and brush, mirroring the MyPaint virtual environment. Our results underscore the agent's effectiveness in acquiring policies for high-dimensional continuous action spaces, facilitating the smooth transfer of brush manipulation techniques from simulation to practical, real-world applications.