ReInsVLN: Revisiting Historical Evidence for Training-Free Vision-and-Language Navigation
Motivated by this finding, we propose ReInsVLN, a training-free framework supporting memory construction and utilization in frozen navigators.
ProofPaper ↗
Key points
- Vision-and-language navigation (VLN) relies on retaining and using relevant observations.
- We introduce ReInsBench, a diagnostic benchmark that examines history utilization through controlled continuation trials.
- Coverage- and Instruction-Aware Compression constructs compact visual context through temporal redundancy reduction, coverage-aware protection, and instruction-guided token selection.
- Real-world trials on humanoid and quadruped robots further support the applicability of ReInsVLN to sequential instruction following and ordinal navigation.
Sources (1)
- [1]ReInsVLN: Revisiting Historical Evidence for Training-Free Vision-and-Language NavigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:08 PM
Motivated by this finding, we propose ReInsVLN, a training-free framework supporting memory construction and utilization in frozen navigators.
Vision-and-language navigation (VLN) relies on retaining and using relevant observations.
Extractive summary: sentences quoted from the sources.
Before this
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