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Machines, Volume 12, Issue 6 (June 2024) – 74 articles

Cover Story (view full-size image): Dexterous manipulation of unknown objects using mechanical hands is a challenge; the main difficulties arise from the absence of precise models of the manipulated objects and limited sensing capabilities of the mechanical hands compared to human hands. This problem was addressed at IOC-UPC by introducing a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-fuzzy Inference System (ANFIS). The proposed planner allows the manipulation of objects of various shapes, sizes, and material properties, providing an adaptive solution to increase robotic dexterity. The planner was validated in real-world scenarios using an Allegro anthropomorphic robotic hand with satisfactory operational outcomes. View this paper
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