Forward Trajectory Steering for Hamilton-Jacobi Reachability Analysis
Hamilton-Jacobi (HJ) reachability provides a mathematically rigorous framework for safe control of dynamical systems, but its practical application is bottlenecked by the computational complexity of solving Hamilton-Jacobi-Isaacs variational inequality PDEs in high dimensions. Physics-informed neural networks (PINNs) have recently emerged as a promising alternative to classical mesh-based solvers, yet their performance is highly sensitive to the choice of collocation sampling. In order to learn accurate safety value functions, existing PINNs-based HJ reachability solvers must rely on complex training pipelines and auxiliary supervision. In this work, we propose STEER2REACH (S2R), a PINNs-...
| Authors | Sungje Park, Stephen Tu |
|---|---|
| Primary category | eess.SY |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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