Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments
IROS• 2016
Abstract
In this work, we present an approach to learn cost maps for driving in
complex urban environments from a very large number of demonstrations of
driving behaviour by human experts. The learned cost maps are constructed
directly from raw sensor measurements, bypassing the effort of manually
designing cost maps as well as features. When deploying the learned cost maps,
the trajectories generated not only replicate human-like driving behaviour but
are also demonstrably robust against systematic errors in putative robot
configuration. To achieve this we deploy a Maximum Entropy based, non-linear
IRL framework which uses Fully Convolutional Neural Networks (FCNs) to
represent the cost model underlying expert driving behaviour. Using a deep,
parametric approach enables us to scale efficiently to large datasets and
complex behaviours by being run-time independent of dataset extent during
deployment. We demonstrate the scalability and the performance of the proposed
approach on an ambitious dataset collected over the course of one year
including more than 25k demonstration trajectories extracted from over 120km of
driving around pedestrianised areas in the city of Milton Keynes, UK. We
evaluate the resulting cost representations by showing the advantages over a
carefully manually designed cost map and, in addition, demonstrate its
robustness to systematic errors by learning precise cost-maps even in the
presence of system calibration perturbations.