Large-scale cost function learning for path planning using deep inverse reinforcement learning
We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of...
| Huvudupphovsmän: | , , , , |
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| Materialtyp: | Journal article |
| Publicerad: |
SAGE Publications
2017
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