Deformable solid objects such as clay or dough are prevalent in industrial and home environments. However, robotic manipulation of such objects has largely remained unexplored in literature due to the high complexity involved in representing and modeling their deformation. This work addresses the problem of shaping elasto-plastic dough by proposing to use a novel elastic end-effector to roll dough in a reinforcement learning framework. The transition model for the end-effector-to-dough interactions is learned from one hour of robot exploration, and doughs of different hydration levels are rolled out into varying lengths. Experimental results are encouraging, with the proposed framework accomplishing the task of rolling out dough into a specified length with 60% fewer actions than a heuristic method. Furthermore, we show that estimating stiffness using the soft end-effector can be used to effectively initialize models, improving robot performance by approximately 40% over incorrect model initialization.
翻译:粘土或面团等可变形固态物体在工业和家用环境中很普遍。然而,由于展示和模拟这些物体变形所涉及的复杂程度很高,对此类物体的机器人操纵在文献中基本上仍未被探索。这项工作解决了形成 Elasto 塑性面团的问题,建议使用新型弹性终效器在强化学习框架内滚动面团。从一个小时的机器人探索中学习了最终效应到剂量相互作用的过渡模型,不同水分水平的面团被推出到不同的长度中。实验结果令人鼓舞,拟议的框架完成了将面团挤到特定长度的任务,其动作比脂质法少60%。此外,我们表明,使用软末效器来估计坚硬性,可以用软末效器有效初始模型,使机器人的性能比不正确的模型初始化率提高约40%。