EffiNav combines depth and vision-language inputs for efficient object goal navigation, matching or exceeding baselines on success rate and path-length-weighted success across simulation benchmarks and real-robot tests.
Navigation with vlm framework: Go to any language, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
REIS reduces inference redundancy in embodied robotic planning via lightweight gating and routing while preserving task performance on ALFRED and real robots.
citing papers explorer
-
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
EffiNav combines depth and vision-language inputs for efficient object goal navigation, matching or exceeding baselines on success rate and path-length-weighted success across simulation benchmarks and real-robot tests.
-
On-Device Robotic Planning: Eliminating Inference Redundancy for Efficient Decision-Making
REIS reduces inference redundancy in embodied robotic planning via lightweight gating and routing while preserving task performance on ALFRED and real robots.