Adaptive architecture improves object navigation in robot environments
F2A-ObjectNav: An Adaptive Agentic Architecture for Object-Goal Navigation
Robotics
Summary
Finding objects in new places is hard for robots because they have to explore, spot the object, and confirm it’s the right one. The authors studied different fixed ways to do this and found none worked perfectly in all situations. To fix this, they created a smart system that adapts by switching methods and remembering past info while keeping normal tasks simple. Their system works well in three different simulated robot environments, adjusting its approach based on challenges it faces.
What this means in practice
- •For robotics development teams: Improve robot navigation to find and confirm objects in unfamiliar indoor spaces using adaptive strategies and memory.
- •For virtual environment designers: Create realistic object navigation challenges that test adaptive robotic systems across diverse simulated environments.
Authors
Seyed Ali Mirghasemi, Alireza Taheri
Abstract
Object-goal navigation requires an embodied agent to explore an unfamiliar environment, identify a target object, navigate toward candidate instances, and reliably determine whether the observed object satisfies the mission goal. This work investigates this problem through a two-stage development process. In Stage I, a fixed modular framework is used to examine combinations of exploration, object detection, and target validation methods. The results reveal clear trade-offs among navigation performance, validation reliability, execution time, and model-related cost, with no single fixed configuration remaining preferable across all evaluated conditions. These observations motivate F2A-ObjectNav (Fixed-to-Adaptive Object-Goal Navigation), an adaptive agentic architecture in which a rule-based Supervisor coordinates specialized components for navigation and exploration, object detection, target validation, semantic guidance, and Mission Memory. The architecture uses event-driven recovery, strategy revision, candidate management, and conditional semantic guidance while retaining lightweight execution during normal operation. F2A-ObjectNav is evaluated over 25 episodes in each of AI2-THOR, RoboTHOR, 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 across the three environments, with higher intervention frequencies observed in RoboTHOR and ProcTHOR than in AI2-THOR.