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.
A decision tree approach to data classification using signal temporal logic
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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.
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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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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.