CLOSER: Evidence-Aware Active Visual Verification for Embodied Object-Goal Navigation

Key points

• Object-goal navigation (ObjectNav) requires an embodied agent to find an instance of a specified category in an unseen environment and stop near it.1
• The agent typically relies on an object detector but must judge online from a limited egocentric video stream, sometimes a single frame, whether a detected candidate is the target.1
• CLOSER fine-tunes an open-source VLM to label each view of a candidate tracked across frames as target, non-target, or uncertain, selects complementary views with an embedding-based diversity gate, and fuses their per-view probabilities with a lightweight network.1
• On Gibson and Habitat-Matterport 3D, it raises average success rate by 3.6 points and success weighted by path length by 2.0 points, showing that verifying evidence before committing improves embodied navigation.1

Sources (1)

• [1]CLOSER: Evidence-Aware Active Visual Verification for Embodied Object-Goal Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:27 PM

  We present CLOSER, an evidence-aware active verification framework that treats stopping as closed-loop evidence acquisition, coupling vision-language model (VLM) verification with active perception.

  Object-goal navigation (ObjectNav) requires an embodied agent to find an instance of a specified category in an unseen environment and stop near it.

Extractive summary: sentences quoted from the sources.