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Probabilistic Reach-Avoid Value Function (PVF): A Temporal-Difference Learning Method for Computing Probabilistic Reach-Avoid Sets

Proposes a self-supervised method, the Probabilistic Reach-Avoid Value Function (PVF), for computing reach-avoid sets for robot tasks under stochastic, potentially unbounded disturbances, applicable to black-box systems. Compared with Hamilton-Jacobi reachability (HJR)-based baselines on systems of increasing complexity, up to the Hopper locomotion task, results show that when disturbances are non-adversarial, PVF's probabilistic reach-avoid sets are less conservative and more accurate than adversarial HJR sets. The method has been submitted to ICRA 2027.

Part of the event: PVF Method Uses Temporal-Difference Learning to Compute Probabilistic Reach-Avoid Sets

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