Learning to Pour
Document Type
Article
Publication Date
5-2017
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
arXiv:1705.09021, p. 1-7.
Scholar Commons Citation
Huang, Yongqiang and Sun, Yu, "Learning to Pour" (2017). Computer Science and Engineering Faculty Publications. 76.
https://digitalcommons.usf.edu/esb_facpub/76
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