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• W3203042892 abstract "In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed." @default.
• W3203042892 created "2021-10-11" @default.
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• W3203042892 date "2021-01-01" @default.
• W3203042892 modified "2023-10-18" @default.
• W3203042892 title "Robot Learning-based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds" @default.
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• W3203042892 doi "https://doi.org/10.1109/access.2021.3118209" @default.
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