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Deformable solid objects such as clay or dough are prevalent in industrialand home environments. However, robotic manipulation of such objects haslargely remained unexplored in literature due to the high complexity involvedin representing and modeling their deformation. This work addresses the problemof shaping elasto-plastic dough by proposing to use a novel elasticend-effector to roll dough in a reinforcement learning framework. Thetransition model for the end-effector-to-dough interactions is learned from onehour of robot exploration, and doughs of different hydration levels are rolledout into varying lengths. Experimental results are encouraging, with theproposed framework accomplishing the task of rolling out dough into a specifiedlength with 60% fewer actions than a heuristic method. Furthermore, we showthat estimating stiffness using the soft end-effector can be used toeffectively initialize models, improving robot performance by approximately 40%over incorrect model initialization.

Deformable Elasto-Plastic Object Shaping using an Elastic Hand and Model-Based Reinforcement Learning