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- W2914157382 abstract "We propose a deep learning based method for fast and responsive human-robot handovers that generate robot motion according to human motion observations. Our method learns an offline human-robot interaction model through a Recurrent Neural Network with Long Short-Term Memory units (LSTM-RNN). The robot uses the learned network to respond appropriately to novel online human motions. Our method is tested both on pre-recorded data and real-world human-robot handover experiments. Our method achieves robot motion accuracies that outperform the baseline. In addition, our method demonstrates a strong ability to adapt to changes in velocity of human motions." @default.
- W2914157382 created "2019-02-21" @default.
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- W2914157382 date "2018-11-01" @default.
- W2914157382 modified "2023-10-12" @default.
- W2914157382 title "Collaborative Human-Robot Motion Generation Using LSTM-RNN" @default.
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- W2914157382 doi "https://doi.org/10.1109/humanoids.2018.8625068" @default.
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