ScissorBot: Learning Generalizable Scissor Skill for Paper Cutting via Simulation, Imitation, and Sim2Real

Jiangran Lyu, Yuxing Chen, Tao Du, Feng Zhu, Huiquan Liu, Yizhou Wang, He Wang

Keywords: Deformable Object Manipulation, Imitation Learning, Sim-to-Real

TL;DR: The first learning-based robotic system for paper cutting with scissors

Abstract:

This paper tackles the challenging robotic task of generalizable paper cutting us-
ing scissors. In this task, scissors attached to a robot arm are driven to accurately
cut curves drawn on the paper, which is hung with the top edge fixed. Due to the
frequent paper-scissor contact and consequent fracture, the paper features contin-
ual deformation and changing topology, which is diffult for accurate modeling.
To deal with such versatile scenarios, we propose ScissorBot, the first learning-
based system for robotic paper cutting with scissors via simulation, imitation
learning and sim2real. Given the lack of sufficient data for this task, we build
PaperCutting-Sim, a paper simulator supporting interactive fracture coupling with
scissors, enabling demonstration generation with a heuristic-based oracle policy.
To ensure effective execution, we customize an action primitive sequence for im-
itation learning to constrain its action space, thus alleviating potential compound-
ing errors. Finally, by integrating sim-to-real techniques to bridge the gap between
simulation and reality, our policy can be effectively deployed on the real robot.
Experimental results demonstrate that our method surpasses all baselines in both
simulation and real-world benchmarks and achieves performance comparable to
human operation with a single hand under the same conditions.

Submission Number: 19