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.
Snopt: An sqp algorithm for large-scale constrained optimization
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GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
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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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GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.