ScissorBot: シミュレーション・模倣・Sim2Realによる汎用性のあるハサミ裁断スキルの学習
ScissorBot: Learning Generalizable Scissor Skill for Paper Cutting via Simulation, Imitation, and Sim2Real
ロボットアームに取り付けたハサミで紙に描かれた曲線を正確に裁断するタスクに取り組み、行動プリミティブ列による模倣学習とSim2Real技術を組み合わせて実機で人間に匹敵する性能を達成した。
著者: Jiangran Lyu, Yuxing Chen, Tao Du, Feng Zhu, Huiquan Liu, Yizhou Wang, He Wang
分類: cs.RO
原文アブストラクト
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 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 achieves performance comparable to human operation with a single hand under the same conditions.
