Research paper
Reachability updates · Recomputing safety as models improve
Decomposition, warm starts and adaptive grids reduce the computation needed to update Hamilton–Jacobi safe sets as uncertainty changes. Evaluation uses simulated two- and ten-dimensional near-hover quadcopters in wind.
Reviewed 2026-11-05 · Source and bibliography checked
- Original title
- Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
- Publisher
- IEEE
- Authors
- Sylvia Herbert, Jason J. Choi, Suvansh Sanjeev, Marsalis Gibson, Koushil Sreenath, Claire J. Tomlin
- DOI
- 10.1109/icra48506.2021.9561561
- Publication date
- 2021-05-30
- Source language
- en
- Edition
- 2021 IEEE International Conference on Robotics and Automation (ICRA); 2021; DOI 10.1109/icra48506.2021.9561561
- Access
- Publisher metadata and open author manuscript
- Publication status
- Published
Related resources and engineering notes
Source overview
Decomposition, warm starts and adaptive grids reduce the computation needed to update Hamilton–Jacobi safe sets as uncertainty changes. Evaluation uses simulated two- and ten-dimensional near-hover quadcopters in wind.
Use and review
Checked archive abstract and publication metadata. Timing and safe sets depend on decomposition and disturbance assumptions; simulation results do not establish hardware performance or arbitrary high-dimensional scalability.
Sources & editions
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability ↗
IEEE · 2021 IEEE International Conference on Robotics and Automation (ICRA); 2021; DOI 10.1109/icra48506.2021.9561561
ICRA 2021 bibliography; author archive abstract https://arxiv.org/abs/2101.05916v2. Registered proceedings date retained.