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Cyber-physical systems (CPS) play a pivotal role in industrial automation, transportation, and critical infrastructure, where meeting stringent timing constraints is essential to ensure operational safety and efficiency. While reinforcement learning (RL) has shown promise in synthesizing controllers for time-critical applications, existing approaches often prioritize speed (as soon as possible, or ASAP) without explicitly addressing deadline compliance. This misalignment can lead to unsafe or suboptimal behaviors, which are unacceptable in industrial contexts requiring both safety and reliability under hard deadlines. For example, with inappropriate rewards, a control policy for a industrial robot can be encouraged to be less safe but fast instead of being steady and meeting the deadlines. To address this challenge, we investigate the relationship between ASAP behavior and deadline-safe behavior, introducing a novel Markov decision process formulation (R-MDP) that includes time-awareness while preserving the Markov property. We propose a reward design method that systematically encourages deadline compliance and guarantees safety in reach-avoid tasks. Our approach is validated on multiple benchmarks, including linear and nonlinear systems representative of industrial applications, such as DC motor control and real-time attitude control. Experimental results demonstrate the efficacy of our method in achieving deadline-safe control while maintaining system safety, offering a reliable solution for industrial CPS where failure to meet deadlines can have severe consequences. 

Medical cyber-physical systems (MCPS) are increasingly adopting learning-enabled components (LECs) to enhance their decision-making capabilities. Due to the safety-critical nature of MCPS, these systems must maintain high performance on both expected and unexpected input data. Therefore, ensuring the robustness of LE-MCPS is crucial for their successful deployment. Existing research predominantly focuses on robustness tosynthetic adversarial examples, crafted by adding imperceptible perturbations to clean input data. However, these synthetic adversarial examples do not accurately reflect the most challenging real-world scenarios, especially in the context of healthcare data. Consequently, robustness to synthetic adversarial examples may not necessarily translate to robustness againstnaturally occurring adversarial examples. We propose a method to evaluate the robustness of LE-MCPS to natural adversarial examples. The method curates naturally adversarial datasets leveraging probabilistic labels obtained from automated weakly supervised labeling which combines noisy and cheap-to-obtain labeling heuristics. Based on these labels, the method adversarially orders the input data and uses this ordering to construct a sequence of increasingly adversarial datasets for assessing robustness. Our evaluation on six MCPS case studies and two non-medical case studies demonstrates (1) the efficacy and statistical validity of our approach to generating naturally adversarial datasets and (2) the utility of our robustness evaluation in classifying robust and non-robust LE-MCPS. 

Neural network (NN)-based control policies have proven their advantages in cyber-physical systems (CPS). When an NN-based policy fails to fulfill a formal specification, engineers leverage NN repair algorithms to fix its behaviors. However, such repair techniques risk breaking the existing correct behaviors, losing not only correctness but also verifiability of initial state subsets. That is, the repair may introduce new risks, previously unaccounted for. In response, we formalize the problem of Repair with Preservation (RwP) and develop Incremental Simulated Annealing Repair (ISAR). ISAR is an NN repair algorithm that aims to preserve correctness and verifiability—while repairing as many failures as possible. Our algorithm leverages simulated annealing on a barriered energy function to safeguard the already-correct initial states while repairing as many additional ones as possible. Moreover, formal verification is utilized to guarantee the repair results. ISAR is compared to a reviewed set of state-of-the-art algorithms, including (1) reinforcement learning-based techniques (STLGym and F-MDP), (2) supervised learning-based techniques (MIQP and minimally deviating repair) and (3) online shielding techniques (tube MPC shielding). Upon evaluation on two standard benchmarks, OpenAI Gym mountain car and an unmanned underwater vehicle, ISAR not only preserves correct behaviors from previously verified initial state regions, but also repairs 81.4% and 23.5% of broken state spaces in the two benchmarks. Moreover, the signal temporal logic (STL) robustness of the ISAR-repaired policies is higher than the baselines. 

Recent advancements in person recognition have raised concerns about identity privacy leaks. Gait recognition through millimeter-wave radar provides a privacy-centric method. However, it is challenged by lower accuracy due to the sparse data these sensors capture. We are the first to investigate a cross-modal method, IdentityKD, to enhance gait-based person recognition with the assistance of facial data. IdentityKD involves a training process using both gait and facial data, while the inference stage is conducted exclusively with gait data. To effectively transfer facial knowledge to the gait model, we create a composite feature representation using contrastive learning. This method integrates facial and gait features into a unified embedding that captures the unique identityspecific information from both modalities. We employ two distinct contrastive learning losses. One minimizes the distance between embeddings of data pairs from the same person, enhancing intraclass compactness, while the other maximizes the distance between embeddings of data pairs from different individuals, improving inter-class separability. Additionally, we use an identity-wise distillation strategy, which tailors the training process for each individual, ensuring that the model learns to distinguish between different identities more effectively. Our experiments on a dataset of 36 subjects, each providing over 5000 face-gait pairs, demonstrate that IdentityKD improves identity recognition accuracy by 6.5% compared to baseline methods.