Distributionally Robust and Safe Imitation Learning
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Computer Science > Machine Learning
Title:Distributionally Robust and Safe Imitation Learning
Abstract:Imitation learning (IL) has achieved remarkable success in complex decision-making tasks. However, its performance is highly sensitive to distribution shifts, which can pose significant safety risks. We propose a distributionally robust and safe IL framework that explicitly addresses both policy-induced and uncertainty-induced distribution shifts. Our approach develops a unified framework leveraging Taylor Series Imitation Learning (TaSIL) to mitigate policy-induced shifts and distributionally robust adaptive control to handle uncertainty-induced shifts. This architecture enables the formulation of an IL problem that optimizes performance under distributional uncertainty while systematically accounting for safety constraints. We demonstrate the effectiveness of the proposed approach on an unmanned aerial vehicle (UAV) case study where the UAV performs a task in an uncertain environment while avoiding unsafe regions.
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2607.13436 [cs.LG] |
| (or arXiv:2607.13436v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13436
arXiv-issued DOI via DataCite (pending registration)
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