Robotics: Science and Systems XXII

Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers

Keyi Shen, Glen Chou

Abstract:

Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty is difficult, especially when the NNs are components within the closed-loop system. Existing reachability tools offer formal over-approximations, yet are often non-differentiable, overly conservative, and too slow to integrate into modern learning and real-time planning pipelines. To address this, we present a parallelizable, differentiable reachability analysis tool in JAX that unifies continuous- and discrete-time systems and supports both analytical and NN-based dynamics and controllers. Our reachability tool uses Taylor-model flowpipe construction and CROWN-style linear bound relaxation and propagation, yielding a GPU-batched reachability primitive that can be differentiated and used in downstream objectives. Building on this primitive, we design (i) a certified training method that encourages the learning of reachability-friendly dynamics models and controllers, and (ii) a reachability-informed sampling-based MPC scheme that incorporates certified reachable sets during action selection and enables gradient-based refinement. Experiments on non-prehensile object manipulation and quadrotor control tasks show competitive performance to baseline planners while providing tight, certified reachability guarantees under uncertainty.

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Bibtex:

  
@INPROCEEDINGS{ShenK-RSS-26, 
    AUTHOR    = {Keyi Shen AND Glen Chou}, 
    TITLE     = {{Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.191} 
}