Abstract
We present a novel approach for fast prediction of human reaching motion in the context of human-robot collaboration in manipulation tasks. The method trains a recurrent neural network to process the three-dimensional hand trajectory and predict the intended target along with its certainty about the position. The network then updates its estimate as it receives more observations while advantaging the positions it is more certain about. To assess the proposed algorithm, we build a library of human hand trajectories reaching targets on a fine grid. Our experiments show the advantage of our algorithm over the state of the art in terms of classification accuracy.
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Acknowledgments
The authors would like to thank Nuo Zhou for her assistance in developing the software and collecting the data.
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Hamandi, M., Hatay, E., Fazli, P. (2018). Predicting the Target in Human-Robot Manipulation Tasks. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_57
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DOI: https://doi.org/10.1007/978-3-030-05204-1_57
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