Satisfaction probabilities for homogeneous stochastic MAS under cLTL admit DFA-based tensor decomposition, enabling a dual-tree value iteration framework that reduces redundant dynamic programming computations.
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Bellman values for temporal logic tasks decompose into a graph of reach-avoid, avoid, and reach-avoid-loop equations solved by embedding the graph in a two-layer neural net (VDPPO) for safe high-dimensional control.
HULK introduces a hierarchical online coordination method for large multi-agent teams executing continual uncertain temporal logic tasks by interleaving subteam assignment and local dynamic execution.
HECTOR introduces a hierarchical human-centric framework with bidirectional interaction, rolling assignment, and dynamic coordination layers for robotic fleets executing continual temporal logic tasks
The authors model gradient descent for optimal control as a separate dynamical system embedded in the plant dynamics to enable reachability-based verification of autonomous systems.
citing papers explorer
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Compressing Correct-by-Design Synthesis for Stochastic Homogeneous Multi-Agent Systems with Counting LTL
Satisfaction probabilities for homogeneous stochastic MAS under cLTL admit DFA-based tensor decomposition, enabling a dual-tree value iteration framework that reduces redundant dynamic programming computations.
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Bellman Value Decomposition for Task Logic in Safe Optimal Control
Bellman values for temporal logic tasks decompose into a graph of reach-avoid, avoid, and reach-avoid-loop equations solved by embedding the graph in a two-layer neural net (VDPPO) for safe high-dimensional control.
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HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks
HULK introduces a hierarchical online coordination method for large multi-agent teams executing continual uncertain temporal logic tasks by interleaving subteam assignment and local dynamic execution.
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HECTOR: Human-centric Hierarchical Coordination and Supervision of Robotic Fleets under Continual Temporal Tasks
HECTOR introduces a hierarchical human-centric framework with bidirectional interaction, rolling assignment, and dynamic coordination layers for robotic fleets executing continual temporal logic tasks
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Verification of Autonomous Systems with Optimal Controllers
The authors model gradient descent for optimal control as a separate dynamical system embedded in the plant dynamics to enable reachability-based verification of autonomous systems.