EvoMemNav builds a Visual-Semantic Memory Graph keeping raw views, applies a budgeted coarse-to-fine policy, and uses reflection-driven updates to improve zero-shot navigation on GOAT-Bench and HM3D.
Stairway to success: Zero-shot floor-aware object-goal navigation via llm-driven coarse-to-fine exploration.arXiv preprint arXiv:2505.23019, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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OneVLA is a unified VLA model using a shared action head and multi-stage progressive training with CoT fine-tuning that reports state-of-the-art results on both navigation and manipulation in simulation and real-world settings.
citing papers explorer
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EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation
EvoMemNav builds a Visual-Semantic Memory Graph keeping raw views, applies a budgeted coarse-to-fine policy, and uses reflection-driven updates to improve zero-shot navigation on GOAT-Bench and HM3D.
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OneVLA: A Unified Framework for Embodied Tasks
OneVLA is a unified VLA model using a shared action head and multi-stage progressive training with CoT fine-tuning that reports state-of-the-art results on both navigation and manipulation in simulation and real-world settings.