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Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner.
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Z. Xu, G. Dhamankar, A. Nair, X. Xiao, G. Warnell, B. Liu, Z. Wang, and P. Stone, “APPLR: Adaptive planner parameter learning from reinforcement,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021
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2021
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2020
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D. Perille, A. Truong, X. Xiao, and P. Stone, “Benchmarking metric ground navigation,” in 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) . IEEE, 2020, pp. 116–121
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H. Karnan, S. Desai, J. P. Hanna, G. Warnell, and P. Stone, “Reinforced grounded action transformation for sim-to-real transfer,” in IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020) , October 2020
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H. Ma, J. S. Smith, and P. A. Vela, “Navtuner: Learning a scene-sensitive family of navigation policies,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 492–499
2021
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X. Xiao, B. Liu, G. Warnell, and P. Stone, “Toward agile maneuvers in highly constrained spaces: Learning from hallucination,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1503–1510, 2021
2021
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2021
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2021
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2021
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2022
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