Learning Parametric Constraints in High Dimensions from Demonstrations Article Swipe
Glen Chou
,
Necmiye Özay
,
Dmitry Berenson
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.03477
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.03477
We present a scalable algorithm for learning parametric constraints in high dimensions from safe expert demonstrations. To reduce the ill-posedness of the constraint recovery problem, our method uses hit-and-run sampling to generate lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a representation of the unsafe set that is compatible with the data by solving an integer program in that representation's parameter space. Our method can either leverage a known parameterization or incrementally grow a parameterization while remaining consistent with the data, and we provide theoretical guarantees on the conservativeness of the recovered unsafe set. We evaluate our method on high-dimensional constraints for high-dimensional systems by learning constraints for 7-DOF arm, quadrotor, and planar pushing examples, and show that our method outperforms baseline approaches.
Related Topics
Concepts
Leverage (statistics)
Parametric statistics
Scalability
Computer science
Mathematical optimization
Representation (politics)
Set (abstract data type)
Constraint (computer-aided design)
Baseline (sea)
Sampling (signal processing)
Integer (computer science)
Integer programming
Algorithm
Artificial intelligence
Mathematics
Statistics
Computer vision
Law
Programming language
Database
Geology
Political science
Geometry
Filter (signal processing)
Oceanography
Politics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1910.03477
- https://arxiv.org/pdf/1910.03477
- OA Status
- green
- Cited By
- 3
- References
- 28
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2979545489
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https://openalex.org/W2979545489Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1910.03477Digital Object Identifier
- Title
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Learning Parametric Constraints in High Dimensions from DemonstrationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-08Full publication date if available
- Authors
-
Glen Chou, Necmiye Özay, Dmitry BerensonList of authors in order
- Landing page
-
https://arxiv.org/abs/1910.03477Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1910.03477Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1910.03477Direct OA link when available
- Concepts
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Leverage (statistics), Parametric statistics, Scalability, Computer science, Mathematical optimization, Representation (politics), Set (abstract data type), Constraint (computer-aided design), Baseline (sea), Sampling (signal processing), Integer (computer science), Integer programming, Algorithm, Artificial intelligence, Mathematics, Statistics, Computer vision, Law, Programming language, Database, Geology, Political science, Geometry, Filter (signal processing), Oceanography, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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