Resilient Control of Cyber-Physical Systems with Distributed Learning - January 2022

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.

Papers in preparation and submission:

NeuReach: Learning Reachability Functions from Simulations.
Dawei Sun and Sayan Mitra, To appear in the proceedings of Int. Conf. on Tools and Algorithms for Construction and Analysis of Systems (TACAS), 2022.

Multi-agent Motion Planning from Signal Temporal Logic Specifications.
Dawei Sun, Jingkai Chen, Sayan Mitra, Chuchu Fan to appear in the proceedings of IEEE Robotics and Automation
Letters (RA-L), 2022.

Verification and Parameter Synthesis for Stochastic Systems using Optimistic Optimization, Negin Musavi, Dawei Sun, Sayan Mitra, Sanjay Shakkottai, and Geir Dullerud, to appear in Proceedings of IEEE Conference on Control Technology and Applications (CCTA), September 2021.

Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence and Parameter Synthesis for Stochastic Systems using Optimistic Optimization, Joao Jansch-Porto, Bin Hu, and Geir Dullerud, submitted for review, January 2021.

MLEFlow: Learning from His- tory to Improve Load Balancing in Tor, H. Darir, H. Sibai, C.-Y. Cheng, N. Borisov, G.E. Dullerud, and S. Mitra, accepted to Privacy Enhancing Technologies Symposium(PETS), 2022.

Linear Bandit Algorithms with Sublinear Time Complexity, Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit Dhillon and Sujay Sanghavi, submitted for review, 2021.

Multi-Agent Low-Dimensional Linear Bandits, Ronshee Chawla, Abishek Sankararaman, and Sanjay Shakkottai. Submitted for review, 2021.

A Model-free Adversarial Reinforcement Learning Approach for mu Synthesis, by Darioush Keivan, Aaron Havens, Peter Seiler, Geir E. Dullerud, Bin Hu, Submitted for Review.

Revisiting PGD Attack for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control, by Aaron Havens, Darioush Keivan, Peter Seiler, Geir E. Dullerud, Bin Hu, Submitted for Review.

GRILC: Gradient-based Reprogrammable Iterative Learning Control for Autonomous Systems, Kuan-Yu Tseng, Jeff S. Shamma, Geir E. Dullerud, Appeared at NeurIPS Workshop on Deployable Decision Making in Embodied Systems, 2021.

Low-fidelity Gradient Updates for High-fidelity Reprogrammable Iterative Learning Control, by Kuan-Yu Tseng, Jeff S. Shamma, Geir E. Dullerud, Submitted for Review.

Related recent and forthcoming publications:

Optimistic Optimization for Statistical Model Checking with Regret Bounds, Negin Musavi, Dawei Sun, Sayan Mitra, Sanjay Shakkottai, and Geir Dullerud, July 2020. Presented at the workshop on Symbolic and Numerical methods for Reasoning about Cyber-Physical Systems.

Full version available online from https://arxiv.org/abs/1911.01537 HOOVER tool available from: https://github.com/sundw2014/HooVer

Verifying Cyber-Physical Systems: A Path to Safe Autonomy, Sayan Mitra, Published by MIT Press, February 16, 2021.

Warm Starting Bandits with Side Information from Confounded Data, N. Sharma, S. Basu, K. Shanmugam and S. Shakkottai, arXiv 2002.08405, 2020. Available at: https://arxiv.org/abs/2002.08405

L2-gain Analysis of Periodic, Event-Triggered Control and Self-Triggered Control using Lifting, N. Strijbosch, G.E.Dullerud, A.Teel, M.Heemels", to appear IEEE Transactions on Automatic Control, 2021.

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

• Mitra is serving as the General Chair of HoTSoS 22.
• Participated in virtual roundtable on "Formal methods for cyber-physical systems", appeared in the IEEE Computer magazine, 2021.

EDUCATIONAL ADVANCES

None to report.