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1. Fast Attack Recovery for Stochastic Cyber-Physical Systems

   Zhang, Lin; Burbano, Luis; Chen, Xin; Cardenas, Alvaro A; Drager, Steven; Adderson, Matthew; Kong, Fanxin (May 2024, IEEE Real-Time and Embedded Technology and Applications Symposium)

   Cyber-physical systems tightly integrate computational resources with physical processes through sensing and actuating, widely penetrating various safety-critical domains, such as autonomous driving, medical monitoring, and industrial control. Unfortunately, they are susceptible to assorted attacks that can result in injuries or physical damage soon after the system is compromised. Consequently, we require mechanisms that swiftly recover their physical states, redirecting a compromised system to desired states to mitigate hazardous situations that can result from attacks. However, existing recovery studies have overlooked stochastic uncertainties that can be unbounded, making a recovery infeasible or invalidating safety and real-time guarantees. This paper presents a novel recovery approach that achieves the highest probability of steering the physical states of systems with stochastic uncertainties to a target set rapidly or within a given time. Further, we prove that our method is sound, complete, fast, and has low computational complexity if the target set can be expressed as a strip. Finally, we demonstrate the practicality of our solution through the implementation in multiple use cases encompassing both linear and nonlinear dynamics, including robotic vehicles, drones, and vehicles in high-fidelity simulators. 

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2. Vision AI-based human-robot collaborative assembly driven by autonomous robots

   https://doi.org/10.1016/j.cirp.2024.03.004  

   Liu, Sichao; Zhang, Jianjing; Wang, Lihui; Gao, Robert X (July 2024, CIRP Annals)

   Autonomous robots that understand human instructions can significantly enhance the efficiency in human-robot assembly operations where robotic support is needed to handle unknown objects and/or provide on-demand assistance. This paper introduces a vision AI-based method for human-robot collaborative (HRC) assembly, enabled by a large language model (LLM). Upon 3D object reconstruction and pose establishment through neural object field modelling, a visual servoing-based mobile robotic system performs object manipulation and navigation guidance to a mobile robot. The LLM model provides text-based logic reasoning and high-level control command generation for natural human-robot interactions. The effectiveness of the presented method is experimentally demonstrated. 

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3. Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

   Benton, Gregory; Maddox, Wesley; Wilson, Andrew Gordon (July 2022, Proceedings of the 39th International Conference on Machine Learning)

   A broad class of stochastic volatility models are defined by systems of stochastic differential equations, and while these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. To address this fundamental limitation, we show how to re-cast a class of stochastic volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. This GP model retains the inductive biases of the stochastic volatility model while providing the posterior predictive distribution given by GP inference. Within this framework, we take inspiration from well studied domains to introduce a new class of models, Volt and Magpie, that significantly outperform baselines in stock and wind speed forecasting, and naturally extend to the multitask setting. 

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4. The Peeping Eye in the Sky

   Yue, Qinggang; Li, Zupei; Gao, Chao; Yu, Wei; Fu, Xinwen; Zhao, Wei (December 2018, IEEE Global Communications Conference)

   In this paper, we investigate the threat of drones equipped with recording devices, which capture videos of individuals typing on their mobile devices and extract the touch input such as passcodes from the videos. Deploying this kind of attack from the air is significantly challenging because of camera vibration and movement caused by drone dynamics and the wind. Our algorithms can estimate the motion trajectory of the touching finger, and derive the typing pattern and then touch inputs. Our experiments show that we can achieve a high success rate against both tablets and smartphones with a DJI Phantom drone from a long distance. A 2.5" NEUTRON mini drone flies outside a window and also achieves a high success rate against tablets behind the window. To the best of our knowledge, we are the first to systematically study drones revealing user inputs on mobile devices and use the finger motion trajectory alone to recover passcodes typed on mobile devices. 

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5. Risk-Aware Stochastic Control of a Sailboat

   https://doi.org/10.23919/ACC60939.2024.10644458  

   Wang, MingYi; Patnaik, Natasha; Somalwar, Anne; Wu, Jingyi; Vladimirsky, Alexander (July 2024, IEEE, 2024 American Control Conference (ACC), Toronto, ON, Canada, 2024)

   Not AvailableSailboat path-planning is a natural hybrid control problem (due to continuous steering and occasional “tack-switching” maneuvers), with the actual path-to-target greatly affected by stochastically evolving wind conditions. Previous studies have focused on finding risk-neutral policies that minimize the expected time of arrival. In contrast, we present a robust control approach, which maximizes the probability of arriving before a specified deadline/threshold. Our numerical method recovers the optimal risk-aware (and threshold-specific) policies for all initial sailboat positions and a broad range of thresholds simultaneously. This is accomplished by solving two quasi-variational inequalities based on second-order Hamilton-Jacobi-Bellman (HJB) PDEs with degenerate parabolicity. Monte-Carlo simulations show that risk-awareness in sailing is particularly useful when a carefully calculated bet on the evolving wind direction might yield a reduction in the number of tack-switches. 

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