aiqbench analysis
This paper uses inverse reinforcement learning to recover control barrier functions from unlabeled expert observations, so the learned reward doubles as a safety constraint. The authors claim the recovered barrier generalizes to unsafe states absent from the demonstrations, and they compare planning-based and policy-based methods on simulated navigation and a real obstacle-avoidance task. For ML readers, the key questions are how far the generalization claim holds beyond these tasks and how the safety guarantees behave under distribution shift.
Discriminative Barrier Functions for Safe Adversarial Imitation Learning from Observation
arXiv:2607.13938v2 Announce Type: replace Abstract: Inverse Reinforcement Learning (IRL) algorithms are powerful tools for learning from and generalizing expert demonstrations, but they often rely on unconstrained exploration, rendering them unsafe for real-world deployment. Meanwhile, Control Barrier Functions (CBFs) can guarantee the safety of control systems, but the analytical design of CBFs can be time-consuming and esoteric. In this work, we address these limitations jointly by constraining reward function candidacy during IRL to the space of CBFs, yielding a formulation that exhibits safe online control with continuous experiential improvement. Crucially, this framework enables the data-driven recovery of barrier functions directly from unlabeled expert observations. We demonstrate that the recovered barrier function is robust to unsafe states entirely absent from the expert data. Furthermore, we benchmark our method against standard IRL baselines in a simulated navigation environment, demonstrating improved safety performance. Finally, we investigate the trade-offs of planning-based versus policy-based IRL methods across both simulation and a real world obstacle avoidance task.
Abstract:Inverse Reinforcement Learning (IRL) algorithms are powerful tools for learning from and generalizing expert demonstrations, but they often rely on unconstrained exploration, rendering them unsafe for real-world deployment. Meanwhile, Control Barrier Functions (CBFs) can guarantee the safety of control systems, but the analytical design of CBFs can be time-consuming and esoteric. In this work, we address these limitations jointly by constraining reward function candidacy during IRL to the space of CBFs, yielding a formulation that exhibits safe online control with continuous experiential improvement. Crucially, this framework enables the data-driven recovery of barrier functions directly from unlabeled expert observations. We demonstrate that the recovered barrier function is robust to unsafe states entirely absent from the expert data. Furthermore, we benchmark our method against standard IRL baselines in a simulated navigation environment, demonstrating improved safety performance. Finally, we investigate the trade-offs of planning-based versus policy-based IRL methods across both simulation and a real world obstacle avoidance task.
Robotics (cs.RO)
arXiv:2607.13938 [cs.RO]
(or arXiv:2607.13938v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.13938 Focus to learn more arXiv-issued DOI via DataCite
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