# Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

- Source: arXiv cs.RO
- Published: 2026-10-28T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/m2jsqq2g3p5ob70p5hs4tlqi1
- Original: https://arxiv.org/abs/2609.14087

## Summary

The authors propose a discrete-time neural reachability framework that learns backward reachable tubes (BRTs) and backward reach-avoid tubes (BRATs) through Bellman-Isaacs value propagation, and uses gradient-free value probes to construct approximate teacher actions, turning adversarial actor learning into supervised policy learning.