GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models
To tackle these challenges, we propose GG-Nav, a simple yet effective navigation framework that learns a structured chain-of-thought (CoT) mechanism via supervised fine-tuning to explicitly localize goal-relevant instances, accumulate grounding evidence, and support more reliable decision-making.
ProofPaper ↗
Key points
- Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models.
- However, these models still have unsatisfactory performance on goal-oriented navigation settings.
- In this paper, we first conduct a motivating study revealing that the key issue lies in the insufficient utilization of streaming observations and imprecise target localization.
- We also deploy GG-Nav on a Unitree Go2 robot, demonstrating its zero-shot generalization capability in unseen real-world environments.
Sources (1)
- [1]GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 03:54 AM
To tackle these challenges, we propose GG-Nav, a simple yet effective navigation framework that learns a structured chain-of-thought (CoT) mechanism via supervised fine-tuning to explicitly localize goal-relevant instances, accumulate grounding evidence, and support more reliable decision-making.
Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models.
Extractive summary: sentences quoted from the sources.
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