Learning Autonomy: Off-Road Navigation Enhanced by Human Input
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915286927540224 |
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| author | Nagariya, Akhil Filev, Dimitar Saripalli, Srikanth Pandey, Gaurav |
| author_facet | Nagariya, Akhil Filev, Dimitar Saripalli, Srikanth Pandey, Gaurav |
| contents | In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18760 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Autonomy: Off-Road Navigation Enhanced by Human Input Nagariya, Akhil Filev, Dimitar Saripalli, Srikanth Pandey, Gaurav Robotics Artificial Intelligence Machine Learning In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology. |
| title | Learning Autonomy: Off-Road Navigation Enhanced by Human Input |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.18760 |