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They utilize the kinematic structure and passive dynamics of the body to adapt to objects of varying shapes and properties. However, these soft passive interactions make the state of the robotic device influenced by the environment, making control generation and state estimation difficult. This work presents a closed\u2010loop framework for dynamic interaction\u2010based grasping that relies on two novelties: 1) a wrist\u2010driven passive soft anthropomorphic hand that can generate robust grasp strategies using one\u2010step kinaesthetic teaching and 2) a learning\u2010based perception system that uses temporal data from sparse tactile sensors to predict and adapt to failures before it happens. With the anthropomorphic soft design and wrist\u2010driven control, it is shown that controllers can be generated robust to novel objects and location uncertainty. 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