Complex Terrain Navigation via Model Error Prediction Article Swipe
Adam Polevoy
,
Craig Knuth
,
Katie M. Popek
,
Kapil D. Katyal
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.09768
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.09768
Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischaracterizing deformable objects as rigid and impassable. Instead, we learn to predict an estimate of traversability of terrain regions and to prefer regions that are easier to navigate (e.g., short grass over small shrubs). Rather than predicting collisions, we instead regress on realized error compared to a canonical dynamics model. We train with an on-policy approach, resulting in successful navigation policies using as little as 50 minutes of training data split across simulation and real world. Our learning-based navigation system is a sample efficient short-term planner that we demonstrate on a Clearpath Husky navigating through a variety of terrain including grassland and forest
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.09768
- https://arxiv.org/pdf/2111.09768
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226301433
All OpenAlex metadata
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https://openalex.org/W4226301433Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.09768Digital Object Identifier
- Title
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Complex Terrain Navigation via Model Error PredictionWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-11-18Full publication date if available
- Authors
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Adam Polevoy, Craig Knuth, Katie M. Popek, Kapil D. KatyalList of authors in order
- Landing page
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https://arxiv.org/abs/2111.09768Publisher landing page
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https://arxiv.org/pdf/2111.09768Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2111.09768Direct OA link when available
- Concepts
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Terrain, Planner, Computer science, Artificial intelligence, Motion planning, Path (computing), Plan (archaeology), Computer vision, Variety (cybernetics), Robot, Geography, Cartography, Archaeology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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