WildOS: Open-Vocabulary Object Search in the Wild
Overview
This research presents WildOS, a unified system for autonomous navigation in complex outdoor environments that combines safe geometric exploration with semantic visual reasoning. The system utilizes a sparse navigation graph maintained by a foundation-model-based vision module (ExploRFM) to score frontier nodes for traversability and object similarity, enabling real-time onboard semantic navigation. Additionally, a particle-filter-based method is introduced for coarse localization of open-vocabulary target queries beyond the robot's immediate depth horizon. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Control: reported autonomous
“ate depth horizon, enabling effective planning toward distant goals. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy. Our results highlight the potential of vision foundation models to drive open-world robotic behaviors tha”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalWildOS: Open-Vocabulary Object Search in the Wild
This research presents WildOS, a unified system for autonomous navigation in complex outdoor environments that combines safe geometric exploration with semantic visual reasoning. The system utilizes a sparse navigation graph maintained by a foundation-model-based vision module (ExploRFM) to score frontier nodes for traversability and object similarity, enabling real-time onboard semantic navigation. Additionally, a particle-filter-based method is introduced for coarse localization of open-vocabulary target queries beyond the robot's immediate depth horizon. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).