  This paper focuses on inverse reinforcement learning (IRL) for autonomous
robot navigation using semantic 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, which
infers semantic class probabilities from the observation sequence, and a cost
encoder, defined as deep neural network over the semantic features. Since the
expert cost is not directly observable, the representation parameters can only
be optimized by differentiating the error between demonstrated controls and a
control policy computed from the cost estimate. The error is optimized using a
closed-form subgradient computed only over a subset of promising states via a
motion planning algorithm. We show that our approach learns to follow traffic
rules in the autonomous driving CARLA simulator by relying on semantic
observations of cars, sidewalks and road lanes.
