Aug. 25, 2022, 1:10 a.m. | Xiao Zang, Miao Yin, Lingyi Huang, Jingjin Yu, Saman Zonouz, Bo Yuan

cs.LG updates on arXiv.org arxiv.org

Neural network (NN)-based methods have emerged as an attractive approach for
robot motion planning due to strong learning capabilities of NN models and
their inherently high parallelism. Despite the current development in this
direction, the efficient capture and processing of important sequential and
spatial information, in a direct and simultaneous way, is still relatively
under-explored. To overcome the challenge and unlock the potentials of neural
networks for motion planning tasks, in this paper, we propose STP-Net, an
end-to-end learning framework …

arxiv motion planning network neural network planning prediction robot temporal video

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