arXiv:2605.30639v1 Announce Type: cross Abstract: Embodied agents have made strong progress in navigating to target objects, but reaching the goal vicinity does not guarantee that the agent has found the correct instance: subtle attribute differences (e.g., "white floral" vs. "white striped") often require close-range, multi-view inspection. We address this gap with Active Instance Verification (AIV), a task in which an agent actively selects viewpoints around a candidate object to decide whether it matches a fine-grained natural-language description. We formalize AIV as a finite-horizon decis
The proliferation of advanced embodied AI agents and robots necessitates more robust verification mechanisms for real-world tasks, which this benchmark addresses.
This development is crucial for advancing the reliability and practical application of embodied AI, enabling agents to perform complex, fine-grained tasks with higher accuracy and autonomy.
Embodied AI agents will gain enhanced capabilities for meticulous object identification and verification, moving beyond simple navigation to nuanced interaction with the physical world.
- · AI/Robotics Developers
- · Logistics/Manufacturing Sector
- · Search & Rescue Operations
- · Tasks requiring manual, fine-grained inspection
- · Early-stage embodied AI without robust verification
Embodied agents will be able to perform tasks requiring precise object identification with greater consistency.
This capability can accelerate the deployment of autonomous robots in complex environments, reducing human intervention.
Improved object verification could lead to new applications in quality control, elder care, and complex assembly where subtle distinctions matter.
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Read at arXiv cs.AI