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Reachability Analysis for Black-Box Dynamical Systems
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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.
Forward citations
Cited by 2 Pith papers
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On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals
Training-conditional conformal prediction does not estimate Bernoulli probabilities and can trivially satisfy its PAC guarantee, making it unsuitable for statistical safety certification.
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DualGuard MPPI: Safe and Performant Optimal Control by Combining Sampling-Based MPC and Hamilton-Jacobi Reachability
DualGuard-MPPI filters every sampled control sequence with a Hamilton-Jacobi safety filter, producing all-safe rollouts and empirically better performance than existing MPPI methods.
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