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 paper-cutting system with scissors

Abstract:

This paper tackles the challenging robotic task of generalizable paper cutting using 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 continual 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 imitation learning to constrain its action space, thus alleviating potential compounding 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 achives performance comparable to human operation with a single hand under the same conditions.

Website: https://pku-epic.github.io/ScissorBot/

Student Paper: yes

Submission Number: 583