F2A-ObjectNav: An Adaptive Agentic Architecture for Object-Goal Navigation
F2A-ObjectNav: An Adaptive Agentic Architecture for Object-Goal Navigation
This research presents F2, an adaptive agentic architecture for object-goal navigation, which uses a rule-based Supervisor to coordinate specialized components for exploration, object detection, and target validation. The system is evaluated in AI2-THOR, RoboTHOR, and ProcTHOR, achieving success rates of 0.84, 0.40, and 0.44, respectively, with additional behavioral metrics analyzing adaptive interventions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
success rate
0.84
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5success-weighted path length
0.52
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5success rate
0.4
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5success-weighted path length
0.22
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5success rate
0.44
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5success-weighted path length
0.37
View original evidence
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
, and ProcTHOR, achieving success rate (SR) / success-weighted path length (SPL) values of 0.84/0.52, 0.40/0.22, and 0.44/0.37, respectively. In addition to task-level metrics, architecture-specific behavioral metrics are used to characterize semantic-guidance usage, strategy switching, recovery activation, memory-supported behavior, and decision stability. The results show different patterns of adaptive intervention
Open source S5
across the three environments, with higher intervention frequencies observed in RoboTHOR and ProcTHOR than in AI2-THOR.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org