Visible to the public Resilient Control of Cyber-Physical Systems with Distributed Learning - July 2022Conflict Detection Enabled

Submitted by mikeprosise on Sat, 07/09/2022 - 10:44am

PI(s) and Co-PI(s): Sayan Mitra and Geir Dullerud and Sanjay Shakkotai (U. Texas at Austin)

Researchers: Dawei Sun and Negin Musavi

HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.

Resiliency: Effective verification of safety and security properties of autonomous and cyber-physical systems

Metrics: How much data is necessary to achieve a certain level of confidence regarding a safety/security claim

PUBLICATIONS
Papers written as a result of your research from the current quarter only.

None to report.

KEY HIGHLIGHTS
Each effort should submit one or two specific highlights. Each item should include a paragraph or two along with a citation if available. Write as if for the general reader of IEEE S&P.
The purpose of the highlights is to give our immediate sponsors a body of evidence that the funding they are providing (in the framework of the SoS lablet model) is delivering results that "more than justify" the investment they are making.

We are developing safety and security analysis approaches for real-life of autonomous and cyber-physical systems using statistical and machine learning techniques. Our approaches rely on distributed and sample-efficient optimization techniques that have been developed in the context of the Multi-armed bandit problem. We have shown how these optimization algorithms can be used effectively for statistical model checking of markov decision processes and hybrid systems. We have built a suite of benchmarks related to online safety analysis of autonomous and semi-autonomous vehicles. Our initial results are very promising as the data usage and the running time of our algorithms can be several orders of magnitude better than existing model checking approaches such as Storm and Prism. Two PhD students are dedicating their research time to the project and the prototype tool has been made available online.

COMMUNITY ENGAGEMENTS

We developed and organized the GRAIC Autonomous Racing Competition which was co-located with CPSWeek 2021. The live event had more than 80 registered members and 30+ attendees. The software framework has been made available to the community for research.

We have been presenting monthly project updates to our project's champions Stephanie Polczynski and Jason Hogue.

Mitra

  • The GRAIC 22 competition was successfully held as part of CPSWeek 22.
  • A mini-version of the GRAIC Autonomous racing competition was demonstrated to the public at the Engineering Open House at Illinois, May 8-9th 2022.
  • Mitra served as the Program Co-Chair of International Conference on Cyber-Physical Systems (ICCPS) which was part of CPSWeek 2022.
  • Mitra served as the General Chair of HoTSoS 22. May 5-7 2022.
  • Shakkottai has conducted a week-long workshop on Causal Inference, January 2022. This was a bootcamp that focused on models for causality, and their applciations to machine learning.
  • Dullerud presentation at POSTECH, Korea; "Learning for Safety and Control in Dynamical Systems", April, 2022.

EDUCATIONAL ADVANCES

We are creating and organizing a Summer School for highschool students in August 2022, supported by the SoS program and Illinois WYSE (Worldwide Youth in Science and Engineering). All the educational material, code, and presentations will be made publicly available.

Groups: