MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation
Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-horizon trajectories due to context-length limits, redundant historical observations, and unstable spatial alignment across time.
ProofPaper ↗
Key points
- To address these limitations, we propose MemoryVLN, a unified memory-driven navigation system built around memory construction and management.
- During memory construction, Long Sequence Compression employs forgetting-curve-inspired temporal sampling to preserve dense recent evidence and sparse yet representative distant observations under a limited token budget.
- Ego-Aware Memory Representation further integrates 2D semantic tokens, 3D geometric tokens, and 6D agent poses, allowing each memory to encode both what was observed and where it was observed.
- During memory management, Hierarchical Memory Bank maintains in-episode and context-level memories through cross-context warm-start and confidence-controlled write-back.
Sources (1)
- [1]MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language NavigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 02:40 PM
Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-horizon trajectories due to context-length limits, redundant historical observations, and unstable spatial alignment across time.
To address these limitations, we propose MemoryVLN, a unified memory-driven navigation system built around memory construction and management.
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