机器人用学习力控完成纸包装，抗材料差异不撕不皱
Robotic Paper Wrapping by Learning Force Control
- 结合模仿与强化学习，让机器人学人类动作并优化受力
- 实测撕裂率和起皱率显著降低，任务成功率高
- 适配不同纸张材质和物体尺寸，工业实用性强
使用包裹纸的机器人包装面临巨大挑战，源于材料复杂的变形特性。包装过程包含多个步骤，主要为折纸或形成折痕。机器人末端轨迹或力矢量的微小偏差都会导致纸张撕裂或起皱，且材料属性差异加剧此问题。本研究提出一种新框架，融合模仿学习与强化学习，使机器人能高效执行每一步包装操作。该框架允许机器人基于人类示范的工具中心点（TCP）近似轨迹运动，同时优化力控参数，防止撕裂或起皱，即使面对不同材质的包装纸也有效。通过消融实验验证，该方法成功完成任务，撕裂与起皱率显著下降。此外，所提力控策略对不同包装纸材质具有适应性，并在目标物体尺寸变化时表现出鲁棒性。
原文摘要 · Abstract (English)
Robotic packaging using wrapping paper poses significant challenges due to the material's complex deformation properties. The packaging process itself involves multiple steps, primarily categorized as folding the paper or creating creases. Small deviations in the robot's arm trajectory or force vector can lead to tearing or wrinkling of the paper, exacerbated by the variability in material properties. This study introduces a novel framework that combines imitation learning and reinforcement learning to enable a robot to perform each step of the packaging process efficiently. The framework allows the robot to follow approximate trajectories of the tool-center point (TCP) based on human demonstrations while optimizing force control parameters to prevent tearing or wrinkling, even with variable wrapping paper materials. The proposed method was validated through ablation studies, which demonstrated successful task completion with a significant reduction in tear and wrinkle rates. Furthermore, the force control strategy proved to be adaptable across different wrapping paper materials and robust against variations in the size of the target object.
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