3 articles tagged with #safety-guarantees. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce VASO, a framework that combines formal verification with self-evolving language model skills for robot control, achieving 97.2% specification compliance on physical tasks. The approach bridges formal methods and foundation models by using counterexamples from model checking as optimization feedback for skill contracts rather than modifying underlying model weights.
Researchers introduce SafeAdapt, a novel framework for updating reinforcement learning policies while maintaining provable safety guarantees across changing environments. The approach uses a 'Rashomon set' to identify safe parameter regions and projects policy updates onto this certified space, addressing the critical challenge of deploying RL agents in safety-critical applications where dynamics and objectives evolve over time.
Researchers propose a new reinforcement learning framework that balances safety and performance in control systems by introducing high-order reciprocal-based control barrier functions and gradient manipulation techniques. The approach enables optimal control of nonlinear systems subject to constraints and unknown disturbances while maintaining robust safety guarantees without requiring prior knowledge of disturbance bounds.
