A predicate-erosion framework using contraction-based probabilistic reachable tubes turns chance-constrained STL planning for stochastic nonlinear systems into deterministic trajectory optimization that achieves high-probability specification satisfaction.
Control barrier functions for signal temporal logic tasks,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
SACBFs guarantee continuous-time safety and finite-time reach-and-remain under zero-order-hold control by estimating inter-sample barrier evolution with Taylor upper bounds and adding a relaxed variant for multiple constraints.
A general framework for multi-agent control that achieves decentralization without dynamical coupling and provides convergence guarantees for time-varying objectives, demonstrated on formation control, coverage, and safe navigation.
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.
citing papers explorer
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Feedback Motion Planning for Stochastic Nonlinear Systems with Signal Temporal Logic Specifications
A predicate-erosion framework using contraction-based probabilistic reachable tubes turns chance-constrained STL planning for stochastic nonlinear systems into deterministic trajectory optimization that achieves high-probability specification satisfaction.
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Sampling-Aware Control Barrier Functions for Safety-Critical and Finite-Time Constrained Control
SACBFs guarantee continuous-time safety and finite-time reach-and-remain under zero-order-hold control by estimating inter-sample barrier evolution with Taylor upper bounds and adding a relaxed variant for multiple constraints.
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Disentangled Control of Multi-Agent Systems
A general framework for multi-agent control that achieves decentralization without dynamical coupling and provides convergence guarantees for time-varying objectives, demonstrated on formation control, coverage, and safe navigation.
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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.