Ezeji, O; Ziegltrum, M; Turrisi, G; Belvedere, T; Modugno, V; (2025) BC-MPPI: A Probabilistic Constraint Layer for Safe Model-Predictive Path-Integral Control. In: Agents and Robots for reliable Engineered Autonomy. AREA 2025. (pp. pp. 131-143). Springer, Cham

Text RA_L_FeedbackMPPI (1).pdf - Accepted Version Access restricted to UCL open access staff until 13 October 2026. Download (5MB)

Abstract

Model Predictive Path Integral (MPPI) control has recently emerged as a fast, gradient-free alternative to model-predictive control in highly non-linear robotic tasks, yet it offers no hard guarantees on constraint satisfaction. We introduce Bayesian-Constraints MPPI (BC-MPPI), a lightweight safety layer that attaches a probabilistic surrogate to every state and input constraint. At each re-planning step the surrogate returns the probability that a candidate trajectory is feasible; this joint probability scales the weight given to a candidate, automatically down-weighting rollouts likely to collide or exceed limits and pushing the sampling distribution toward the safe subset; no hand-tuned penalty costs or explicit sample rejection required. We train the surrogate from 1, 000 offline simulations and deploy the controller on a quadrotor in MuJoCo with both static and moving obstacles. Across K∈[100,1500] rollouts BC-MPPI preserves safety margins while satisfying the prescribed probability of violation. Because the surrogate is a stand-alone, version-controlled artefact and the runtime safety score is a single scalar, the approach integrates naturally with verification-and-validation pipelines for certifiable autonomous systems.

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