Robust waypoint prediction is crucial for mobile robots operating in open-world, safety-critical settings. While Imitation Learning (IL) methods have demonstrated great success in practice, they are susceptible to distribution shifts: the policy can become dangerously overconfident in unfamiliar states. In this paper, we present ELLIPSE, a method building on multivariate deep evidential regression to output waypoints and multivariate Student-t predictive distributions in a single forward pass. To reduce covariate-shift-induced overconfidence under viewpoint and pose perturbations near expert trajectories, we introduce a lightweight domain augmentation procedure that synthesizes plausible viewpoint/pose variations without collecting additional demonstrations. To improve uncertainty reliability under environment/domain shift (e.g., unseen staircases), we apply a post-hoc isotonic recalibration on probability integral transform (PIT) values so that prediction sets remain plausible during deployment. We ground the discussion and experiments in staircase waypoint prediction, where obtaining robust waypoint and uncertainty is pivotal. Extensive real world evaluations show that ELLIPSE improves both task success rate and uncertainty coverage compared to baselines.
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Figure 1: Deployment environments ( Section IV-A ). Such environments are especially challenging due to limited sensor FOV, narrow passageways (landings), and invisible stair boundaries (hollow handrails and glass). The ability to navigate stairs is pivotal for robots to efficiently explore multi-floor structures.
Figure 2: Overall pipeline of ELLIPSE . The network is trained offline with domain augmentation to improve robustness of the waypoints and uncertainties ( Section III-B ). During inference, the predicted waypoint distributions ( Section III-A ) are recalibrated using scales obtained from Isotonic Regression ( Section III-C ). A Mahalanobis-distance-based uncertainty-aware MPPI planner runs on a separate thread and tracks a pool of waypoints (at high frequency) while relaxing constraints on uncertain waypoints ( Section III-D ).
Department of Computer Science, University of York, UK · Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus