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.

本文提出了一种新的弹性末端执行器，利用强化学习的框架来滚动各种水合水平的面团，并通过建立末端执行器和面团之间的转移模型使机器人能够正确地操作面团，实验结果表明，相比启发式方法，该方案可以将面团滚出特定长度，且操作次数减少了 60％，同时还表明利用弹性末端执行器估计刚度可以用于有效初始化模型，提高机器人性能约 40％。