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Learning to Explore using Active Neural SLAM
[article]
2020
arXiv
pre-print
This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called 'Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to
arXiv:2004.05155v1
fatcat:6t7hhvlocfa4pel6y2v46tmusm