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.
Statistical safety and robustness guarantees for feedback motion planning of unknown underactuated stochastic systems
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GPU-SLS computes safe robust nonlinear MPC policies online in ~20 ms for up to 75D systems by reachability-constrained system level synthesis accelerated via custom GPU QP solvers.
citing papers explorer
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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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Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
GPU-SLS computes safe robust nonlinear MPC policies online in ~20 ms for up to 75D systems by reachability-constrained system level synthesis accelerated via custom GPU QP solvers.