AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory

Key points

• Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users.1
• Visual depth point-goal navigation leverages verified node arrivals to perform translational re-anchoring, without requiring a robot-constructed global SLAM map.1
• Field-collected perceptual evidence gates memory updates, maintaining an explicit separation between human-specified permissions and robot-observed traversability.1
• Our experiments on a Unitree Go2 show that AVALON with X-NavDP completes a 12.81 m Intersection-to-Dining mission in 46.4 s (5/5) with no mapping, 40.9% less robot time than DimOS-Go2 (78.5 s, 4/5), while policy-only X-NavDP times out in all of the 5 runs.1

Sources (1)

• [1]AVALON: Zero-Shot Indoor Navigation with Human-Seeded Topological Memory
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:33 AM

  We address this human-familiar, robot-unseen setting with AVALON, a topological navigation framework organized around human-seeded layered spatial memory, node-verified topological execution, and task-triggered, evidence-gated human--robot memory co-updating.

  Indoor service robots often enter unmapped buildings whose layouts and task constraints are already familiar to users.

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