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Understanding human behavior is key for robots and intelligent systems that share a space with people.
1909
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2015
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2015
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2015
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2016
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2016
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2017
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2017
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B. Okal and K. O. Arras, “Learning socially normative robot navigation behaviors with bayesian inverse reinforcement learning,” in
2016
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A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human trajectory prediction in crowded spaces,” in
2016
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O. Palinko, F. Rea, G. Sandini, and A. Sciutti, “Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration,” in
2016
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Z. Yan, T. Duckett, and N. Bellotto, “Online learning for human classification in 3D LiDAR-based tracking,” in
2017
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L. Palmieri, T. P. Kucner, M. Magnusson, A. J. Lilienthal, and K. O. Arras, “Kinodynamic motion planning on gaussian mixture fields,” in
2017
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P. A. Lasota, T. Fong, and J. A. Shah, “A survey of methods for safe human-robot interaction,”
2017
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C. S. Swaminathan, T. P. Kucner, M. Magnusson, L. Palmieri, and A. J. Lilienthal, “Down the cliff: Flow-aware trajectory planning under motion pattern uncertainty,” in
2018
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2018
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S. Molina, G. Cielniak, T. Krajník, and T. Duckett, “Modelling and predicting rhythmic flow patterns in dynamic environments,” in
2018
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2019
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S.-Y. Lo, S. Alkoby, and P. Stone, “Robust motion planning and safety benchmarking in human workspaces.” in
2019
Closest in time.
R. T. Chadalavada, H. Andreasson, M. Schindler, R. Palm, and A. J. Lilienthal, “Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human–robot interaction,”
2020
Closest in time.