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Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
[article]
2018
arXiv
pre-print
We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires
arXiv:1710.02543v2
fatcat:j6nkwnl52bcdfaotoy4dzwhroy