A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
Bab-nd: Long-horizon motion planning with branch-and-bound and neural dynamics.arXiv preprint arXiv:2412.09584, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
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Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.
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Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
A JAX-based differentiable reachability primitive for continuous- and discrete-time NN dynamics and controllers that supports certified training and sampling-based MPC with gradient refinement.
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Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial
Tutorial introducing applications of the existing α,β-CROWN verifier to scalable formal verification of neural network controllers via bound computation and domain partitioning.