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- W4295116594 abstract "Pouring is a simple task people perform daily. It is the second most frequently executed motion in cooking scenarios, after pick-and-place. We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring. The approach uses recurrent neural networks as its building blocks. We collected the pouring demonstrations which we used for training. To test our approach in simulation, we also created and trained a force estimation system. The simulated experiments show that the system is able to generalize to single unseen element of the pouring characteristics." @default.
- W4295116594 created "2022-09-10" @default.
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- W4295116594 date "2017-05-24" @default.
- W4295116594 modified "2023-09-26" @default.
- W4295116594 title "Learning to Pour" @default.
- W4295116594 doi "https://doi.org/10.48550/arxiv.1705.09021" @default.
- W4295116594 hasPublicationYear "2017" @default.
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