This paper introduces VICONav, a vectorized configurable object-navigation benchmark for mobile robots in Isaac Lab. The benchmark is built on Isaac Sim and uses USD (Universal Scene Description) assets, physically simulated robot motion, and rendered indoor scenes executed in parallel environment instances. All instances share a common potential object pool, while each reset independently samples the target object, object arrangement, obstacle configuration, and initial robot state. The tensor shapes and object indices remain fixed across episodes, which supports efficient vectorized training and direct comparison of policies under a stable observation interface. Scene generation is specified by a two-level configuration scheme. The first level defines object classes, physical parameters, USD assets, and semantic object tags. The second level defines compositional scene blocks that represent functional zones and object groups. This design separates the object vocabulary from the spatial organization of the scene and allows controlled variation of object composition, target distribution, graph construction, observation modality, and action parameterization. VICONav supports discrete and continuous control, visual and graph-based observations, curriculum-dependent initial-state distributions, and simulator-state access for reward computation and auxiliary training objectives. We evaluate the benchmark through a series of experiments covering learned navigation policies, classical reference methods, and robustness tests under controlled visual and graph perturbations. The results show that VICONav can be used as a configuration-driven experimental substrate for comparing object-goal navigation approaches under controlled changes of scene structure, observation composition, and control interface.
DOI: 10.3103/S1060992X26603738
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Oskolkov, N., Makarov, D. VICONav: Vectorized Isaac Configurable Object Navigation Benchmark // Optical Memory and Neural Networks 35 (Suppl. 1), S112–S125 (2026). https://doi.org/10.3103/S1060992X26603738