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This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent representation.
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N. Das, N. Gupta, and M. Yip, “Fastron: A learning-based configuration space model for rapid collision detection for gross motion planning in changing environments,” in RSS Workshop Data-Driven Manipulation , 2017
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2018
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2016
Cited alongside, same era.
Y. Li, Z. Littlefield, and K. E. Bekris, “Asymptotically optimal sampling-based kinodynamic planning,” Int. Journal of Robotics Research , 2016
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C. Finn and S. Levine, “Deep visual foresight for planning robot motion,” in ICRA , 2017
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E. Schmerling, K. Leung, W. Vollprecht, and M. Pavone, “Multimodal probabilistic model-based planning for human-robot interaction,” in Proc. IEEE Conf. on Robotics and Automation , 2018
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B. Ichter, J. Harrison, and M. Pavone, “Learning sampling distributions for robot motion planning,” in Proc. IEEE Conf. on Robotics and Automation , 2018
2018
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