Sampling-based robust control of autonomous systems with non-Gaussian noise

Thom S. Badings, Alessandro Abate, Nils Jansen, David Parker, Hasan A. Poonawala, Marielle Stoelinga

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Controllers for autonomous systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel planning method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target. First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP, and compute a controller for which these guarantees carry over to the autonomous system. Realistic benchmarks show the practical applicability of our method, even when the iMDP has millions of states or transitions.
Original languageEnglish
Title of host publicationAAAI 2022 Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence
Place of PublicationPalo Alto, California
PublisherAAAI Press
Number of pages11
Volume36 No. 9
ISBN (Electronic)10: 1-57735-876-7
ISBN (Print)13: 978-1-57735-876-3
Publication statusPublished - 28 Jun 2022
Event36th AAAI Conference on Artificial Intelligence - Vancouver, Canada
Duration: 22 Feb 20221 Mar 2022

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


Conference36th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-22


  • Planning, Routing, And Scheduling (PRS),
  • Reasoning Under Uncertainty (RU)
  • Intelligent Robotics (ROB)
  • Constraint Satisfaction And Optimization (CSO)


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