Researchers have introduced NavVerse, a new physics-enabled benchmark designed to evaluate embodied navigation for robots that must transition between indoor and outdoor environments. The benchmark includes 100 indoor scenes, 50 urban outdoor scenes, and 50 combined indoor-to-outdoor scenes, featuring tasks like Object Navigation, Vision-and-Language Navigation, and Place Navigation. Initial experiments with reinforcement learning and modular baselines reveal that current agents struggle with cross-context navigation, with adaptation to new environments remaining a significant bottleneck. AI
IMPACT This benchmark could accelerate the development of more capable robots for real-world applications requiring seamless indoor-outdoor transitions.
RANK_REASON The cluster describes a new benchmark for robot navigation published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- NavVerse
- Object Navigation
- PlaceNav
- Place navigation impaired in rats with hippocampal lesions
- reinforcement learning
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