  Cost-maps are used by robotic vehicles to plan collision-free paths. The cost
associated with each cell in the map represents the sensed environment
information which is often determined manually after several trial-and-error
efforts. In off-road environments, due to the presence of several types of
features, it is challenging to handcraft the cost values associated with each
feature. Moreover, different handcrafted cost values can lead to different
paths for the same environment which is not desirable. In this paper, we
address the problem of learning the cost-map values from the sensed environment
for robust vehicle path planning. We propose a novel framework called as CAMEL
using deep learning approach that learns the parameters through demonstrations
yielding an adaptive and robust cost-map for path planning. CAMEL has been
trained on multi-modal datasets such as RELLIS-3D. The evaluation of CAMEL is
carried out on an off-road scene simulator (MAVS) and on field data from
IISER-B campus. We also perform realworld implementation of CAMEL on a ground
rover. The results shows flexible and robust motion of the vehicle without
collisions in unstructured terrains.
