Rigid-soft robot hybrids promise to unite the precision and repeatability of rigid manipulators with the compliance and adaptability of soft structures. Yet, full-fledged rigid-soft manipulators remain largely unexplored, in part because the intelligence required to exploit this combination is still missing. Here, we introduce a hybrid robotic system obtained by the series interconnection of a rigid arm and a soft manipulator, and systematically examine its ability to perform both established and previously inaccessible manipulation tasks. We show that a tailored imitation learning algorithm -- generally applicable to this class of hybrid systems -- leverages the complementary roles of rigid precision and soft embodiment to acquire manipulation skills and generalize them in a single shot across task variations. As a result, the system matches or exceeds the performance of rigid robots in classical tasks such as stacking, while enabling qualitatively new behaviors, including grasping objects through narrow openings.

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