NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

NavVerse is a physics-enabled benchmark designed for embodied navigation, specifically addressing the complex challenge of indoor-to-outdoor transitions within a single continuous episode. It features 100 indoor scenes, 50 urban outdoor scenes, and 50 hybrid indoor-to-outdoor scenes, comprising 10,000 episodes across Object Navigation (ObjNav), Vision-and-Language Navigation (VLN), and a novel Place Navigation (PlaceNav) task. Agents are evaluated using executable robot interfaces, considering task-success, path-efficiency, and safety metrics. Zero-shot experiments with RL, VLA, and modular baselines reveal that current agents struggle significantly with cross-context navigation. While end-to-end VLAs achieve the highest zero-shot success, modular methods provide the strongest safety profiles. PlaceNav tasks highlight a substantial performance drop from outdoor to indoor-to-outdoor scenes, indicating that adaptation remains a major bottleneck for robust robot deployment.

Key takeaway

For Robotics Engineers developing embodied navigation systems, you must prioritize benchmarks that simulate continuous indoor-to-outdoor transitions with physics-enabled execution. Current models struggle with adaptation, safety, and efficiency in hybrid scenarios, necessitating holistic evaluation beyond task success. Focus on robust kinodynamic control and multi-context semantic grounding to bridge this critical real-world gap, ensuring your agents can reliably operate across diverse environments.

Key insights

Seamless indoor-to-outdoor robot navigation requires physics-enabled simulation and robust adaptation to diverse environments and kinodynamic constraints.

Principles

• Indoor-to-outdoor transitions pose significant adaptation challenges for embodied agents.
• Realistic navigation benchmarks must account for kinodynamic constraints and robot dynamics.
• Current navigation methods show divergent strengths in success, efficiency, and safety.

Method

NavVerse integrates residential, commercial, outdoor, and hybrid scenes in continuous robot simulation, generating 10,000 episodes across ObjNav, VLN, and PlaceNav, evaluated via task success, path efficiency, and safety metrics.

In practice

• Evaluate agents on exit finding, boundary crossing, and post-transition adaptation.
• Prioritize safety metrics like Collision Rate and Navigable Surface Ratio.
• Test long-horizon PlaceNav tasks for real-world relevance.

Topics

• Embodied Navigation
• Robot Simulation
• Indoor-to-Outdoor Transition
• Benchmarking
• Vision-and-Language Navigation
• Place Navigation
• Kinodynamic Constraints

Code references

• isaac-sim/IsaacSim

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

Related on AIssential

• NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation — NavVerse benchmarks continuous indoor-to-outdoor robot navigation, revealing current agents struggle with cross-context adaptation and safety.
• NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation — NavVerse benchmarks indoor-to-outdoor robot navigation, revealing current agents struggle with cross-context adaptation and safety in complex environments.
• WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents — Long-horizon embodied agents require benchmarks and memory frameworks that handle dynamic, stateful, and partially observable environments.
• Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator — Neural simulation from real-world captures can scale embodied navigation data and improve real-world transfer.
• GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory — Persistent semantic occupancy mapping for embodied agents requires hierarchical memory and causal fusion of transient visual evidence.
• MVP-Nav: Multi-layer Value Map Planner Navigator — MVP-Nav enables physically-aware, RGB-only zero-shot object navigation by integrating 3D foundation models and a Multi-layer Value Map.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.