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Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior.
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2018
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2019
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2019
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2019
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2020
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Z. Yan, S. Schreiberhuber, G. Halmetschlager, T. Duckett, M. Vincze, and N. Bellotto, “Robot perception of static and dynamic objects with an autonomous floor scrubber,”
2020
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2020
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2023
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2023
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2023
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2023
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2023
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2023
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N. Hirose, D. Shah, A. Sridhar, and S. Levine, “Exaug: Robot-conditioned navigation policies via geometric experience augmentation,” in
2023
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2023
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D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine, “Gnm: A general navigation model to drive any robot,” in
2023
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2023
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M. Pfeiffer, U. Schwesinger, H. Sommer, E. Galceran, and R. Siegwart, “Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models,” in
2096
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