Data Scaling for Navigation in Unknown Environments
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917357540081664 |
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| author | Suomela, Lauri Takahata, Naoki Arachchige, Sasanka Kuruppu Edelman, Harry Kämäräinen, Joni-Kristian |
| author_facet | Suomela, Lauri Takahata, Naoki Arachchige, Sasanka Kuruppu Edelman, Harry Kämäräinen, Joni-Kristian |
| contents | Generalization of imitation-learned navigation policies to environments unseen in training remains a major challenge. We address this by conducting the first large-scale study of how data quantity and data diversity affect real-world generalization in end-to-end, map-free visual navigation. Using a curated 4,565-hour crowd-sourced dataset collected across 161 locations in 35 countries, we train policies for point goal navigation and evaluate their closed-loop control performance on sidewalk robots operating in four countries, covering 125 km of autonomous driving.
Our results show that large-scale training data enables zero-shot navigation in unknown environments, approaching the performance of policies trained with environment-specific demonstrations. Critically, we find that data diversity is far more important than data quantity. Doubling the number of geographical locations in a training set decreases navigation errors by ~15%, while performance benefit from adding data from existing locations saturates with very little data. We also observe that, with noisy crowd-sourced data, simple regression-based models outperform generative and sequence-based architectures. We release our policies, evaluation setup and example videos at https://lasuomela.github.io/navigation_scaling/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09444 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Scaling for Navigation in Unknown Environments Suomela, Lauri Takahata, Naoki Arachchige, Sasanka Kuruppu Edelman, Harry Kämäräinen, Joni-Kristian Robotics Generalization of imitation-learned navigation policies to environments unseen in training remains a major challenge. We address this by conducting the first large-scale study of how data quantity and data diversity affect real-world generalization in end-to-end, map-free visual navigation. Using a curated 4,565-hour crowd-sourced dataset collected across 161 locations in 35 countries, we train policies for point goal navigation and evaluate their closed-loop control performance on sidewalk robots operating in four countries, covering 125 km of autonomous driving. Our results show that large-scale training data enables zero-shot navigation in unknown environments, approaching the performance of policies trained with environment-specific demonstrations. Critically, we find that data diversity is far more important than data quantity. Doubling the number of geographical locations in a training set decreases navigation errors by ~15%, while performance benefit from adding data from existing locations saturates with very little data. We also observe that, with noisy crowd-sourced data, simple regression-based models outperform generative and sequence-based architectures. We release our policies, evaluation setup and example videos at https://lasuomela.github.io/navigation_scaling/. |
| title | Data Scaling for Navigation in Unknown Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2601.09444 |