Learning Parametric Constraints in High Dimensions from Demonstrations Article Swipe
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· 2019
· Open Access
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· 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 To Compare & Contrast
- 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