2024/08/27 by Pritam Dash, Ethan Chan, Dash, Pritam +3 · 3 citations
Computer Science · Decision Sciences · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Robotics (cs.RO) #Safety Systems Engineering in Autonomy #Scientific Computing and Data Management #Software Testing and Debugging Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2408.15200
openalex publication_date 2024/08/27 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28
Robotic Autonomous Vehicles (RAVs) rely on their sensors for perception, and follow strict mission specifications (e.g., altitude, speed, and geofence constraints) for safe and timely operations. Physical attacks can corrupt the RAVs' sensors, resulting in mission failures. Recovering RAVs from such attacks demands robust control techniques that maintain compliance with mission specifications even under attacks to ensure the RAV's safety and timely operations. We propose SpecGuard, a technique that complies with mission specifications and performs safe recovery of RAVs. There are two innovations in SpecGuard. First, it introduces an approach to incorporate mission specifications and learn a recovery control policy using Deep Reinforcement Learning (Deep-RL). We design a compliance-based reward structure that reflects the RAV's complex dynamics and enables SpecGuard to satisfy multiple mission specifications simultaneously. Second, SpecGuard incorporates state reconstruction, a technique that minimizes attack induced sensor perturbations. This reconstruction enables effective adversarial training, and optimizing the recovery control policy for robustness under attacks. We evaluate SpecGuard in both virtual and real RAVs, and find that it achieves 92% recovery success rate under attacks on different sensors, without any crashes or stalls. SpecGuard achieves 2X higher recovery success than prior work, and incurs about 15% performance overhead on real RAVs.