  This paper focuses on inverse reinforcement learning for autonomous
navigation using distance and semantic category observations. The objective is
to infer a cost function that explains demonstrated behavior while relying only
on the expert's observations and state-control trajectory. We develop a map
encoder, that infers semantic category probabilities from the observation
sequence, and a cost encoder, defined as a deep neural network over the
semantic features. Since the expert cost is not directly observable, the model
parameters can only be optimized by differentiating the error between
demonstrated controls and a control policy computed from the cost estimate. We
propose a new model of expert behavior that enables error minimization using a
closed-form subgradient computed only over a subset of promising states via a
motion planning algorithm. Our approach allows generalizing the learned
behavior to new environments with new spatial configurations of the semantic
categories. We analyze the different components of our model in a minigrid
environment. We also demonstrate that our approach learns to follow traffic
rules in the autonomous driving CARLA simulator by relying on semantic
observations of buildings, sidewalks, and road lanes.
