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arXiv · 1910.03477

Learning Parametric Constraints in High Dimensions from Demonstrations

Glen Chou·Necmiye Ozay·Dmitry Berenson
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Abstract

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.

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Glen Chou, Necmiye Ozay, Dmitry Berenson. 2019-10-08. Learning Parametric Constraints in High Dimensions from Demonstrations. https://arxiv.org/abs/1910.03477

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