R-DTLGN is a recurrent ternary logic network that hardens polynomial surrogates to monotone-gate circuits, links STL bounded operators to AND/OR connections for stability and principled abstention, and uses a formula-derived bound to size hidden state.
Monitoring temporal properties of con- tinuous signals
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Differentiable SpaTiaL is the first fully tensorized, end-to-end differentiable symbolic spatio-temporal logic framework that enables gradient-based trajectory optimization and parameter learning for robotic manipulation under geometric and temporal constraints.
Ternary logic encodings of temporal behavior trees enable mixed-integer linear programming for correct-by-construction control synthesis in linear systems.
The work introduces k-hop prescribed performance observers that let decentralized controllers enforce cooperative STL specifications on heterogeneous multi-agent systems despite bounded disturbances and limited communication.
citing papers explorer
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On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks
R-DTLGN is a recurrent ternary logic network that hardens polynomial surrogates to monotone-gate circuits, links STL bounded operators to AND/OR connections for stability and principled abstention, and uses a formula-derived bound to size hidden state.
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Differentiable SpaTiaL: Symbolic Learning and Reasoning with Geometric Temporal Logic for Manipulation Tasks
Differentiable SpaTiaL is the first fully tensorized, end-to-end differentiable symbolic spatio-temporal logic framework that enables gradient-based trajectory optimization and parameter learning for robotic manipulation under geometric and temporal constraints.
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Ternary Logic Encodings of Temporal Behavior Trees with Application to Control Synthesis
Ternary logic encodings of temporal behavior trees enable mixed-integer linear programming for correct-by-construction control synthesis in linear systems.
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Control of Multi-agent Systems under STL Specifications based on Prescribed Performance Observers
The work introduces k-hop prescribed performance observers that let decentralized controllers enforce cooperative STL specifications on heterogeneous multi-agent systems despite bounded disturbances and limited communication.