Reachability Analysis for Black-Box Dynamical Systems
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Abstract (arXiv)
Hamilton-Jacobi (HJ) reachability analysis is a powerful framework for ensuring safety and performance in autonomous systems. However, existing methods typically rely on a white-box dynamics model of the system, limiting their applicability in many practical robotics scenarios where only a black-box model of the system is available. In this work, we propose a novel reachability method to compute reachable sets and safe controllers for black-box dynamical systems. Our approach efficiently approximates the Hamiltonian function using samples from the black-box dynamics. This Hamiltonian is then used to solve the HJ Partial Differential Equation (PDE), providing the reachable set of the system. The proposed method can be applied to general nonlinear systems and can be seamlessly integrated with existing reachability toolboxes for white-box systems to extend their use to black-box systems. Through simulation studies on a black-box slip-wheel car and a quadruped robot, we demonstrate the effectiveness of our approach in accurately obtaining the reachable sets for blackbox dynamical systems.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Oct 2024.
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2026Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guaranteescited by 1
- 2025On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervalscited by 0
- 2026Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximizationcited by 0
- 2026Unifying Goal-Conditioned RL and Unsupervised Skill Learning via Control-Maximization7 May 2026
- 2026Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees3 Jan 2026
- 2025On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals11 Feb 2025