aiqbench analysis
This research advances safety-critical control for nonlinear systems by integrating adversarial reinforcement learning into robust control barrier functions, enabling safe operation under black-box dynamics without relying on explicit system models. The approach significantly expands the certified safe operating envelope compared to traditional methods while maintaining smooth safety enforcement in high-dimensional robotic simulators. By lifting barrier certificates into state-action space, the framework removes structural limitations that previously hindered scalability in complex environments.
Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning
arXiv:2604.13192v2 Announce Type: replace-cross Abstract: Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g., control-affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying only conservative subsets of the maximal robust safe set. In this paper, we introduce a new robust CBF framework for general nonlinear systems under bounded uncertainty. We first show that the safety value function solving the dynamic programming Isaacs equation is a valid robust discrete-time CBF that enforces safety on the maximal robust safe set. We then adopt the key reinforcement learning (RL) notion of quality function (or Q-function), which removes the need for explicit dynamics by lifting the barrier certificate into state-action space and yields a novel robust Q-CBF constraint for safety filtering. Combined with adversarial RL, this enables the synthesis and deployment of robust Q-CBFs on general nonlinear systems with black-box dynamics and unknown uncertainty structure. We validate the framework on a canonical inverted pendulum benchmark and a 36-D quadruped simulator, achieving substantially less conservative safe sets than barrier-based baselines on the pendulum and reliable safety enforcement even under adversarial uncertainty realizations on the quadruped.
Abstract:Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g., control-affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying only conservative subsets of the maximal robust safe set. In this paper, we introduce a new robust CBF framework for general nonlinear systems under bounded uncertainty. We first show that the safety value function solving the dynamic programming Isaacs equation is a valid robust discrete-time CBF that enforces safety on the maximal robust safe set. We then adopt the key reinforcement learning (RL) notion of quality function (or Q-function), which removes the need for explicit dynamics by lifting the barrier certificate into state-action space and yields a novel robust Q-CBF constraint for safety filtering. Combined with adversarial RL, this enables the synthesis and deployment of robust Q-CBFs on general nonlinear systems with black-box dynamics and unknown uncertainty structure. We validate the framework on a canonical inverted pendulum benchmark and a 36-D quadruped simulator, achieving substantially less conservative safe sets than barrier-based baselines on the pendulum and reliable safety enforcement even under adversarial uncertainty realizations on the quadruped.
8 pages, 2 figures. Accepted to the 65th IEEE Conference on Decision and Control (CDC)
Systems and Control (eess.SY); Robotics (cs.RO)
arXiv:2604.13192 [eess.SY]
(or arXiv:2604.13192v2 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2604.13192 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 313 words
