EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation
This paper introduces EffiNav, a method that combines depth sensing with vision-language models to achieve efficient object goal navigation in unknown environments. The authors evaluate the approach on two simulation benchmarks—Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal Navigation (OVON)—and further validate its performance on physical robots in real-world settings. Failure analysis is conducted across large-scale simulation episodes, and the method is extended to a memory-augmented navigation task on the GOAT-BENCH dataset. Results show that EffiNav matches or outperforms recent baselines on standard metrics including Success Rate (SR) and Success weighted by Path Length (SPL), demonstrating its efficiency, robustness, and generalizability for autonomous exploration tasks.
The authors evaluate a framework that fuses depth and vision-language models to improve navigation efficiency in unknown environments, reporting results on Habitat Matterport 3D and Open-Vocabulary Object goal Navigation benchmarks with additional validation on physical robots.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org