用仿真+模仿学习让机器人学会通用剪纸，真实效果接近人类单手操作。
ScissorBot: Learning Generalizable Scissor Skill for Paper Cutting via Simulation, Imitation, and Sim2Real
- 设计动作基元序列约束控制空间，减少误差累积。
- 在仿真和真实场景中均超越所有基线方法。
- 适合对复杂柔性物体操作感兴趣的机器人研究者。
本文解决机器人使用剪刀进行通用纸张剪切的挑战性任务。剪刀固定在机械臂上，需沿纸上绘制的曲线精确剪裁，纸张顶部被固定。由于频繁的纸剪接触与断裂，纸张持续变形且拓扑结构改变，难以精确建模。为确保有效执行，我们定制了动作基元序列用于模仿学习，以约束动作空间，缓解潜在误差累积。最终通过集成模拟到现实（Sim2Real）技术，弥合仿真与现实的差距，使策略可有效部署于真实机器人。实验结果表明，该方法在仿真和真实世界基准测试中均优于所有基线，性能接近单手人类操作水平。
原文摘要 · Abstract (English)
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.
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