Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning
This work provides an incremental improvement for autonomous navigation systems by enabling cost function inference from semantic observations, which could benefit developers of self-driving cars.
This paper addresses the problem of inferring a cost function for autonomous navigation from expert demonstrations, using only distance and semantic category observations. The authors developed a map encoder to infer semantic category probabilities and a cost encoder over these features, enabling the model to learn traffic rules in the CARLA simulator based on semantic observations.
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.