Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.11334 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911105979252736 |
|---|---|
| author | Huang, Yuehao Liu, Liang Lei, Shuangming Ma, Yukai Su, Hao Mei, Jianbiao Zhao, Pengxiang Gu, Yaqing Liu, Yong Lv, Jiajun |
| author_facet | Huang, Yuehao Liu, Liang Lei, Shuangming Ma, Yukai Su, Hao Mei, Jianbiao Zhao, Pengxiang Gu, Yaqing Liu, Yong Lv, Jiajun |
| contents | Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots to identify and locate objects based on implicit human intent, even when object locations are unknown. However, traditional data-driven DDN methods rely on pre-collected data for model training and decision-making, limiting their generalization capability in unseen scenarios. In this paper, we propose CogDDN, a VLM-based framework that emulates the human cognitive and learning mechanisms by integrating fast and slow thinking systems and selectively identifying key objects essential to fulfilling user demands. CogDDN identifies appropriate target objects by semantically aligning detected objects with the given instructions. Furthermore, it incorporates a dual-process decision-making module, comprising a Heuristic Process for rapid, efficient decisions and an Analytic Process that analyzes past errors, accumulates them in a knowledge base, and continuously improves performance. Chain of Thought (CoT) reasoning strengthens the decision-making process. Extensive closed-loop evaluations on the AI2Thor simulator with the ProcThor dataset show that CogDDN outperforms single-view camera-only methods by 15\%, demonstrating significant improvements in navigation accuracy and adaptability. The project page is available at https://yuehaohuang.github.io/CogDDN/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking Huang, Yuehao Liu, Liang Lei, Shuangming Ma, Yukai Su, Hao Mei, Jianbiao Zhao, Pengxiang Gu, Yaqing Liu, Yong Lv, Jiajun Artificial Intelligence Robotics I.2.9 Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots to identify and locate objects based on implicit human intent, even when object locations are unknown. However, traditional data-driven DDN methods rely on pre-collected data for model training and decision-making, limiting their generalization capability in unseen scenarios. In this paper, we propose CogDDN, a VLM-based framework that emulates the human cognitive and learning mechanisms by integrating fast and slow thinking systems and selectively identifying key objects essential to fulfilling user demands. CogDDN identifies appropriate target objects by semantically aligning detected objects with the given instructions. Furthermore, it incorporates a dual-process decision-making module, comprising a Heuristic Process for rapid, efficient decisions and an Analytic Process that analyzes past errors, accumulates them in a knowledge base, and continuously improves performance. Chain of Thought (CoT) reasoning strengthens the decision-making process. Extensive closed-loop evaluations on the AI2Thor simulator with the ProcThor dataset show that CogDDN outperforms single-view camera-only methods by 15\%, demonstrating significant improvements in navigation accuracy and adaptability. The project page is available at https://yuehaohuang.github.io/CogDDN/. |
| title | CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking |
| topic | Artificial Intelligence Robotics I.2.9 |
| url | https://arxiv.org/abs/2507.11334 |