Learning Traversability Cost Maps with Decomposed Uncertainties Via Continuous-State MEDIRL

Abstract

Accurate traversability assessment is critical for mobile robot motion planning, yet sensor occlusions and model limitations often compromise cost map reliability. Therefore, analyzing spatial uncertainty is essential for robust risk management. We propose a novel Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework that learns a traversability cost map while explicitly disentangling aleatoric and epistemic uncertainties. Aleatoric uncertainty is captured via latent sampling in a Conditional Variational Autoencoder, while epistemic uncertainty is estimated using a decoder ensemble. For kinematic fidelity, we introduce efficient continuous-state rollouts utilizing precomputed transition grids and bilinear interpolation. Fusing camera and LiDAR features, our model achieves stable convergence guided by a novel margin loss. Results demonstrate that learned state visitation frequencies match expert trajectories, and the decomposed uncertainties effectively identify high-risk terrains, providing a crucial foundation for safer autonomous navigation.