LE-Nav uses MLLM scene descriptions as conditions for a CVAE that generates planner hyperparameters, achieving navigation performance comparable to human experts in real-world tests.
Dynamic adaptive dynamic window approach,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
LE-Nav uses MLLM scene descriptions as conditions for a CVAE that generates planner hyperparameters, achieving navigation performance comparable to human experts in real-world tests.