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This paper presents VLM-Safe-RL, a framework that integrates frozen vision-language models into constrained MDP Lagrangian updates to provide anticipatory cost signals for safe reinforcement learning in high-speed visual control tasks. The method outperforms standard constraint-aware baselines on Safety-Gymnasium FormulaOne L2 and generalizes to held-out environments.
Proposes LILAC+, a framework for safe continual reinforcement learning under nonstationarity that uses three adaptive safety mechanisms: context-based safety constraints, adaptation-speed constraints, and budget-to-state safety enforcement. Evaluations in simulated driving environments show reduced safety violations under distribution shift while maintaining competitive performance.
This paper proposes Action-Conditioned Risk Gating, a lightweight reinforcement learning method for risk-sensitive control under partial observability that uses a compact finite-history proxy state and an action-conditioned near-term risk predictor to balance safety and performance.
This paper presents a framework (CARE) that jointly learns control inputs and communication-efficient timing decisions under a pointwise Lyapunov safety shield, achieving higher inter-sample intervals than classical methods on inverted pendulum, cart-pole, and planar quadrotor systems.