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

Biao Jia; Dinesh Manocha

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

Robotics 2023-09-18 v1

Authors: Biao Jia , Dinesh Manocha

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.

Keywords

sim-to-real transfer imitation learning robotic manipulation

Cite

@article{arxiv.2309.08457,
  title  = {Sim-to-Real Brush Manipulation using Behavior Cloning and Reinforcement Learning},
  author = {Biao Jia and Dinesh Manocha},
  journal= {arXiv preprint arXiv:2309.08457},
  year   = {2023}
}