Pritam Dash (University of British Columbia) and Karthik Pattabiraman (University of British Columbia)

Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. We present PID-Piper , a novel framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient FeedForward Controller (FFC), which runs in tandem with the RV’s primary controller and monitors it. Under attacks, the FFC takes over from the RV’s primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that PID-Piper allows RVs to complete their missions successfully despite attacks in 83% of the cases.

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Demo #6: Impact of Stealthy Attacks on Autonomous Robotic...

Pritam Dash, Mehdi Karimibiuki, and Karthik Pattabiraman (University of British Columbia)

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Grant Hernandez (University of Florida), Marius Muench (Vrije Universiteit Amsterdam), Dominik Maier (TU Berlin), Alyssa Milburn (Vrije Universiteit Amsterdam), Shinjo Park (TU Berlin), Tobias Scharnowski (Ruhr-University Bochum), Tyler Tucker (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

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Zhenxiao Qi (UC Riverside), Yu Qu (UC Riverside), Heng Yin (UC Riverside)

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Christopher DiPalma, Ningfei Wang, Takami Sato, and Qi Alfred Chen (UC Irvine)

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