A horizon-agnostic neural operator paired with a boundary control barrier function creates a real-time safety filter that raises safe trajectory rates by up to 22% on fluid manipulation tasks in simulation.
Control barrier functions: Theory and applications
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A distributed optimization controller uses truncation functions and two-time-scale auxiliary variables to guarantee collision avoidance, connectivity preservation, and target convergence for multi-agent systems under time-varying communication topologies.
Control-theoretic guardrails enable proactive correction of risky LLM agent actions in latent space, preventing catastrophes like collisions or bankruptcy while preserving task performance in simulated environments.
A separate regulator module adaptively scales actions in RL to reduce constraint violations while preserving exploration, yielding up to 126x fewer violations and over 10x higher returns on Safety Gym tasks.
A unified quadratic program combines control barrier functions from AV@R risk with risk-aware expected information gain for safe active perception in 3DGS fields.
PBRS-augmented RL trained in simple settings transfers zero-shot to complex UAV environments when wrapped with a CLF-CBF-QP safety filter, yielding shorter missions and formal safety guarantees.
A CBF-based hierarchical quadratic programming framework enables flexible prioritization of safety and performance tasks for safe physical human-robot interaction, demonstrated on a real redundant robot.
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.
citing papers explorer
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Online Safety Filter for Deformable Object Manipulation with Horizon Agnostic Neural Operators
A horizon-agnostic neural operator paired with a boundary control barrier function creates a real-time safety filter that raises safe trajectory rates by up to 22% on fluid manipulation tasks in simulation.
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Distributed Safety-Critical Control of Multi-Agent Systems with Time-Varying Communication Topologies
A distributed optimization controller uses truncation functions and two-time-scale auxiliary variables to guarantee collision avoidance, connectivity preservation, and target convergence for multi-agent systems under time-varying communication topologies.
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From Refusal to Recovery: A Control-Theoretic Approach to Generative AI Guardrails
Control-theoretic guardrails enable proactive correction of risky LLM agent actions in latent space, preventing catastrophes like collisions or bankruptcy while preserving task performance in simulated environments.
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Constraint-Aware Reinforcement Learning via Adaptive Action Scaling
A separate regulator module adaptively scales actions in RL to reduce constraint violations while preserving exploration, yielding up to 126x fewer violations and over 10x higher returns on Safety Gym tasks.
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Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions
A unified quadratic program combines control barrier functions from AV@R risk with risk-aware expected information gain for safe active perception in 3DGS fields.
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Zero-Shot, Safe and Time-Efficient UAV Navigation via Potential-Based Reward Shaping, Control Lyapunov and Barrier Functions
PBRS-augmented RL trained in simple settings transfers zero-shot to complex UAV environments when wrapped with a CLF-CBF-QP safety filter, yielding shorter missions and formal safety guarantees.
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Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction
A CBF-based hierarchical quadratic programming framework enables flexible prioritization of safety and performance tasks for safe physical human-robot interaction, demonstrated on a real redundant robot.
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Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts
Pre-VLA is a multimodal runtime verifier that predicts safety confidence and advantage scores for action chunks, raising closed-loop success rates on the LIBERO benchmark from 30.79% to 37.62%.