Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty remains difficult, especially for closed-loop NN systems. Existing reachability tools provide formal over-approximations, yet are often non-differentiable, overly conservative, or too slow for modern learning and online planning pipelines. To address this, we present a parallelizable, differentiable reachability framework in JAX for continuous- and discrete-time systems with analytical and NN-based dynamics and controllers. Our framework combines Taylor-model flowpipe construction with CROWN-style linear bound propagation through a unified repre...
| Authors | Keyi Shen, Glen Chou |
|---|---|
| Primary category | cs.RO |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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