  Learning-based methods are increasingly popular for search algorithms in
single-criterion optimization problems. In contrast, for multiple-criteria
optimization there are significantly fewer approaches despite the existence of
numerous applications. Constrained path-planning for Autonomous Ground Vehicles
(AGV) is one such application, where an AGV is typically deployed in disaster
relief or search and rescue applications in off-road environments. The agent
can be faced with the following dilemma : optimize a source-destination path
according to a known criterion and an uncertain criterion under operational
constraints. The known criterion is associated to the cost of the path,
representing the distance. The uncertain criterion represents the feasibility
of driving through the path without requiring human intervention. It depends on
various external parameters such as the physics of the vehicle, the state of
the explored terrains or weather conditions. In this work, we leverage
knowledge acquired through offline simulations by training a neural network
model to predict the uncertain criterion. We integrate this model inside a
path-planner which can solve problems online. Finally, we conduct experiments
on realistic AGV scenarios which illustrate that the proposed framework
requires human intervention less frequently, trading for a limited increase in
the path distance.
