  Path planning plays an essential role in many areas of robotics. Various
planning techniques have been presented, either focusing on learning a specific
task from demonstrations or retrieving trajectories by optimizing for
hand-crafted cost functions which are well defined a priori. In this work, we
present an incremental adversarial learning-based framework that allows
inferring implicit behaviour, i.e. the natural characteristic of a set of given
trajectories. To achieve adversarial learning, a zero-sum game is constructed
between a planning algorithm and an adversary - the discriminator. We employ
the discriminator within an optimal motion planning algorithm, such that costs
can be learned and optimized iteratively, improving the integration of implicit
behavior. By combining a cost-based planning approach with trained intrinsic
behaviour, this can be be integrated also with other constraints such as
obstacles or general cost factors within a single planning framework. We
demonstrate the proposed method on a dataset for collision avoidance, as well
as for the generation of human-like trajectories from motion capture data. Our
results show that incremental adversarial learning is able to generate paths
that reflect the natural implicit behaviour of a dataset, with the ability to
improve on performance using iterative learning and generation.
