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Motion planning and obstacle avoidance is a key challenge in robotics applications.
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Learning Sampling Distributions for Robot Motion Planning
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Motion Planning for Humanoid Robots
K. Harada, E. Yoshida, and K. Yokoi · 2014
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The ycb object and model set: Towards common benchmarks for manipulation research, 2015
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D. P. Kingma and J. Ba · 2015
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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D. Clevert, T. Unterthiner, and S. Hochreiter · 2016
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PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Robot Motion Planning in Learned Latent Spaces
B. Ichter and M. Pavone · 2019
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https://www.di.ens.fr/willow/research/nmp_repr/ , 2020
Learning obstacle representations for neural motion planning, project webpage · 2020
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Learning sampling distributions
V. Pong · 2020
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Implementation of mpnet: Motion planning networks
A. Qureshi · 2020
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Reinforcement learning framework and algorithms implemented in pytorch
V. Pong · 2020
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Implementation of ddpg-mp
T. Jurgenson · 2020
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