Abstract
We present a novel framework for non-prehensile shape manipulation of deformable objects using Deep Reinforcement Learning. Unlike previous approaches that rely on grasping, our method employs a sequence of gentle pushing actions to deform objects into target shapes. We introduce a continuous parametrization of pushing actions that allows for precise control over pushing trajectories, enabling more flexible and efficient manipulation. The framework is applicable to a wide range of objects by representing them as sampled boundary coordinates, removing the need for predefined object partitions. Trained entirely in simulation, our controller demonstrates zero-shot transfer to real-world scenarios without additional training. Extensive evaluations show that our approach not only matches but substantially exceeds the performance of previous methods, while being more gentle and efficient. We demonstrate successful manipulation across various deformable objects and materials, including food items like salmon and pork loin. This work represents a significant advancement in robotic manipulation of deformable objects, with potential applications in food processing, manufacturing, and beyond.