ORION: Ordinal Neural Collapse as a Representation Prior for Visual Navigation

Learning robust navigation policies directly from visual ob-servations remains a fundamental challenge in vision-based robotic nav-igation. In end-to-end imitation learning approaches, the visual encoderand action decoder are jointly optimized using a single action loss, whichprovides only an indirect supervisory signal to the encoder. This indi-rect supervision frequently results in the encoder learning ambiguous,action-agnostic representations. The problem is further complicated bysubstantial variations in scene structure and appearance across diverseenvironments, as well as the prevalence of visual distractors inherent toreal-world navigation settings. Such action-agnostic features cause thenavigation policy to produce inconsistent actions at ambiguous deci-sion points, leading to navigation failure. To overcome these limitations,we propose ORION (Ordinal Neural Collapse for Visual Navigation), amethod that explicitly organizes the encoder’s representation space ac-cording to the ordinal structure of navigation actions. In the context ofgoal-directed navigation, ego-centric control categories from Far Left toFar Right exhibit a natural ordinal relationship in which neighboringclasses share similar visual contexts, while semantically opposing classesdiffer substantially in appearance. We encourage class representations tobe arranged sequentially along a single discriminative axis, while sup-pressing off-axis variance within each class. The pretrained encoder isthen integrated into a diffusion-based navigation framework, and the fullpipeline is fine-tuned end-to-end. Extensive experiments in both simula-tion and real-world settings show that ORION consistently outperformsend-to-end and neural collapse baselines in navigation success rate andgoal progress, with notable gains in visually challenging scenarios suchas complex multi-way intersections.