Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913459467190272 |
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| author | Sivakumar, Arun N. Thangeda, Pranay Fang, Yixiao Gasparino, Mateus V. Cuaran, Jose Ornik, Melkior Chowdhary, Girish |
| author_facet | Sivakumar, Arun N. Thangeda, Pranay Fang, Yixiao Gasparino, Mateus V. Cuaran, Jose Ornik, Melkior Chowdhary, Girish |
| contents | Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03059 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots Sivakumar, Arun N. Thangeda, Pranay Fang, Yixiao Gasparino, Mateus V. Cuaran, Jose Ornik, Melkior Chowdhary, Girish Robotics Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement. |
| title | Learning to Turn: Diffusion Imitation for Robust Row Turning in Under-Canopy Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.03059 |