Pushing the Limits of Reactive Planning: Learning to Escape Local Minima
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913435322679296 |
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| author | Meijer, Isar Pantic, Michael Oleynikova, Helen Siegwart, Roland |
| author_facet | Meijer, Isar Pantic, Michael Oleynikova, Helen Siegwart, Roland |
| contents | When does a robot planner need a map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between fully reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based planner, which should give the robot geometric intuition about how to escape local minima. We train on a large number of extremely cluttered worlds auto-generated from primitive shapes, and show that our system zero-shot transfers to real 3D man-made environments, and can handle up to 30% sensor noise without degeneration of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_13530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pushing the Limits of Reactive Planning: Learning to Escape Local Minima Meijer, Isar Pantic, Michael Oleynikova, Helen Siegwart, Roland Robotics When does a robot planner need a map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between fully reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based planner, which should give the robot geometric intuition about how to escape local minima. We train on a large number of extremely cluttered worlds auto-generated from primitive shapes, and show that our system zero-shot transfers to real 3D man-made environments, and can handle up to 30% sensor noise without degeneration of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation. |
| title | Pushing the Limits of Reactive Planning: Learning to Escape Local Minima |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.13530 |