{"ID":2861809,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00441","arxiv_id":"2510.00441","title":"Seeing through Uncertainty: Robust Task-Oriented Optimization in Visual Navigation","abstract":"Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-based agents typically increase architectural complexity that paradoxically become counterproductive in the small-sample regime. This paper introduce NeuRO, a integrated learning-to-optimize framework that tightly couples perception networks with downstream task-level robust optimization. Specifically, NeuRO addresses core difficulties in this integration: (i) it transforms noisy visual predictions under data scarcity into convex uncertainty sets using Partially Input Convex Neural Networks (PICNNs) with conformal calibration, which directly parameterize the optimization constraints; and (ii) it reformulates planning under partial observability as a robust optimization problem, enabling uncertainty-aware policies that transfer across environments. Extensive experiments on both unordered and sequential multi-object navigation tasks demonstrate that NeuRO establishes SoTA performance, particularly in generalization to unseen environments. Our work thus presents a significant advancement for developing robust, generalizable autonomous agents.","short_abstract":"Visual navigation is a fundamental problem in embodied AI, yet practical deployments demand long-horizon planning capabilities to address multi-objective tasks. A major bottleneck is data scarcity: policies learned from limited data often overfit and fail to generalize OOD. Existing neural network-based agents typicall...","url_abs":"https://arxiv.org/abs/2510.00441","url_pdf":"https://arxiv.org/pdf/2510.00441v3","authors":"[\"Yiyuan Pan\",\"Yunzhe Xu\",\"Zhe Liu\",\"Hesheng Wang\"]","published":"2025-10-01T02:48:28Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}