BC-MPPI: A Probabilistic Constraint Layer for Safe Model-Predictive Path-Integral Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915527502331904 |
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| author | Ezeji, Odichimnma Ziegltrum, Michael Turrisi, Giulio Belvedere, Tommaso Modugno, Valerio |
| author_facet | Ezeji, Odichimnma Ziegltrum, Michael Turrisi, Giulio Belvedere, Tommaso Modugno, Valerio |
| contents | 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 1000 offline simulations and deploy the controller on a quadrotor in MuJoCo with both static and moving obstacles. Across K in [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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00272 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BC-MPPI: A Probabilistic Constraint Layer for Safe Model-Predictive Path-Integral Control Ezeji, Odichimnma Ziegltrum, Michael Turrisi, Giulio Belvedere, Tommaso Modugno, Valerio Robotics 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 1000 offline simulations and deploy the controller on a quadrotor in MuJoCo with both static and moving obstacles. Across K in [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. |
| title | BC-MPPI: A Probabilistic Constraint Layer for Safe Model-Predictive Path-Integral Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.00272 |