CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents
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2025
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| author | Seong, Haebin Kim, Sungmin Cho, Yongjun Joe, Myunchul Kim, Geunwoo Park, Yubeen Kim, Sunhoo Kim, Yoonshik Choi, Suhwan Jung, Jaeyoon Youn, Jiyong Kwak, Jinmyung Ahn, Sunghee Lee, Jaemin Do, Younggil Yi, Seungyeop Cheong, Woojin Oh, Minhyeok Kim, Minchan Kang, Seongjae Seong, Samwoo Yu, Youngjae Lee, Yunsung |
| author_facet | Seong, Haebin Kim, Sungmin Cho, Yongjun Joe, Myunchul Kim, Geunwoo Park, Yubeen Kim, Sunhoo Kim, Yoonshik Choi, Suhwan Jung, Jaeyoon Youn, Jiyong Kwak, Jinmyung Ahn, Sunghee Lee, Jaemin Do, Younggil Yi, Seungyeop Cheong, Woojin Oh, Minhyeok Kim, Minchan Kang, Seongjae Seong, Samwoo Yu, Youngjae Lee, Yunsung |
| contents | While current navigation benchmarks prioritize task success in simplified settings, they neglect the multidimensional economic constraints essential for the real-world commercialization of autonomous delivery systems. We introduce CostNav, an Economic Navigation Benchmark that evaluates physical AI agents through comprehensive economic cost-revenue analysis aligned with real-world business operations. By integrating industry-standard data--such as Securities and Exchange Commission (SEC) filings and Abbreviated Injury Scale (AIS) injury reports--with Isaac Sim's detailed collision and cargo dynamics, CostNav transcends simple task completion to accurately evaluate business value in complex, real-world scenarios. To our knowledge, CostNav is the first physics-grounded economic benchmark that uses industry-standard regulatory and financial data to quantitatively expose the gap between navigation research metrics and commercial viability, revealing that optimizing for task success on a simplified task fundamentally differs from optimizing for real-world economic deployment. Evaluating seven baselines--two rule-based and five imitation learning--we find that no current method is economically viable, all yielding negative contribution margins. The best-performing method, CANVAS (-27.36\$/run), equipped with only an RGB camera and GPS, outperforms LiDAR-equipped Nav2 w/ GPS (-35.46\$/run). We challenge the community to develop navigation policies that achieve economic viability on CostNav. We remain method-agnostic, evaluating success solely on cost rather than the underlying architecture. All resources are available at https://github.com/worv-ai/CostNav. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents Seong, Haebin Kim, Sungmin Cho, Yongjun Joe, Myunchul Kim, Geunwoo Park, Yubeen Kim, Sunhoo Kim, Yoonshik Choi, Suhwan Jung, Jaeyoon Youn, Jiyong Kwak, Jinmyung Ahn, Sunghee Lee, Jaemin Do, Younggil Yi, Seungyeop Cheong, Woojin Oh, Minhyeok Kim, Minchan Kang, Seongjae Seong, Samwoo Yu, Youngjae Lee, Yunsung Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Robotics While current navigation benchmarks prioritize task success in simplified settings, they neglect the multidimensional economic constraints essential for the real-world commercialization of autonomous delivery systems. We introduce CostNav, an Economic Navigation Benchmark that evaluates physical AI agents through comprehensive economic cost-revenue analysis aligned with real-world business operations. By integrating industry-standard data--such as Securities and Exchange Commission (SEC) filings and Abbreviated Injury Scale (AIS) injury reports--with Isaac Sim's detailed collision and cargo dynamics, CostNav transcends simple task completion to accurately evaluate business value in complex, real-world scenarios. To our knowledge, CostNav is the first physics-grounded economic benchmark that uses industry-standard regulatory and financial data to quantitatively expose the gap between navigation research metrics and commercial viability, revealing that optimizing for task success on a simplified task fundamentally differs from optimizing for real-world economic deployment. Evaluating seven baselines--two rule-based and five imitation learning--we find that no current method is economically viable, all yielding negative contribution margins. The best-performing method, CANVAS (-27.36\$/run), equipped with only an RGB camera and GPS, outperforms LiDAR-equipped Nav2 w/ GPS (-35.46\$/run). We challenge the community to develop navigation policies that achieve economic viability on CostNav. We remain method-agnostic, evaluating success solely on cost rather than the underlying architecture. All resources are available at https://github.com/worv-ai/CostNav. |
| title | CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2511.20216 |