The morphology of a slender soft-robot can be modified by sensing its shape via sensors and exerting moments via actuators embedded along its body. The actuating moments required to morph these soft-robots to a desired shape are often difficult to compute due to the geometric non-linearity associated with the structure, the errors in modeling the experimental system, and the limitations in sensing and feedback/actuation capabilities. In this article, we explore the effect of a simple feedback strategy (actuation being proportional to the sensed curvature) on the shape of a soft-robot, modeled as an elastica. The finite number of sensors and actuators, often seen in experiments, is captured in the model via filters of specified widths. Using proportional feedback, we study the simple task of straightening the device by compensating for the sagging introduced by its self-weight. The device undergoes a hierarchy of morphological instabilities defined in the phase-space given by the gravito-bending number, non-dimensional sensing/feedback gain, and the scaled width of the filter. For complex shape-morphing tasks, given a perfect model of the device with limited sensing and actuating capabilities, we find that a trade-off arises (set by the sensor spacing & actuator size) between capturing the long and short wavelength features. We show that the error in shape-morphing is minimal for a fixed filter width when we choose an appropriate actuating gain (whose magnitude goes as a square of the filter width). Our model provides a quantitative lens to study and design slender soft devices with limited sensing and actuating capabilities for complex maneuvering applications.

Industrial Internet of Things environments increasingly rely on advanced Anomaly Detection and explanation techniques to rapidly detect and mitigate cyberincidents, thereby ensuring operational safety. The sequential nature of data collected from these environments has enabled improvements in Anomaly Detection using Machine Learning and Deep Learning models by processing time windows rather than treating the data as tabular. However, conventional explanation methods often neglect this temporal structure, leading to imprecise or less actionable explanations. This work presents ShaTS (Shapley values for Time Series models), which is a model-agnostic explainable Artificial Intelligence method designed to enhance the precision of Shapley value explanations for time series models. ShaTS addresses the shortcomings of traditional approaches by incorporating an a priori feature grouping strategy that preserves temporal dependencies and produces both coherent and actionable insights. Experiments conducted on the SWaT dataset demonstrate that ShaTS accurately identifies critical time instants, precisely pinpoints the sensors, actuators, and processes affected by anomalies, and outperforms SHAP in terms of both explainability and resource efficiency, fulfilling the real-time requirements of industrial environments.

As autonomous systems become integral to various industries, effective strategies for fault handling are essential to ensure reliability and efficiency. Transfer of Control (ToC), a traditional approach for interrupting automated processes during faults, is often triggered unnecessarily in non-critical situations. To address this, we propose a data-driven method that uses human interaction data to train AI models capable of preemptively identifying and addressing issues or assisting users in resolution. Using an interactive tool simulating an industrial vacuum cleaner, we collected data and developed an LSTM-based model to predict user behavior. Our findings reveal that even data from non-experts can effectively train models to reduce unnecessary ToC events, enhancing the system's robustness. This approach highlights the potential of AI to learn directly from human problem-solving behaviors, complementing sensor data to improve industrial automation and human-AI collaboration.

Enhancing Network Security Management in Water Systems using FM-based Attack Attribution Aleksandar Avdalovi c, Joseph Khoury, Ahmad Taha, Elias Bou-Harb Division of Computer Science and Engineering, Louisiana State University, USA Civil and Environmental Engineering, V anderbilt University, USA Abstract --Water systems are vital components of modern infrastructure, yet they are increasingly susceptible to sophisticated cyber attacks with potentially dire consequences on public health and safety. While state-of-the-art machine learning techniques effectively detect anomalies, contemporary model-agnostic attack attribution methods using LIME, SHAP, and LEMNA are deemed impractical for large-scale, interdependent water systems. This is due to the intricate interconnectivity and dynamic interactions that define these complex environments. Such methods primarily emphasize individual feature importance while falling short of addressing the crucial sensor-actuator interactions in water systems, which limits their effectiveness in identifying root cause attacks. T o this end, we propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks. For instance, an anomaly in an actuator pump activity can be attributed to a top root cause attack candidates, a list of water pressure sensors, which is derived from the underlying linear and quadratic effects captured by our approach. In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA. Additionally, our approach maintains strong performance in single-feature attack scenarios, demonstrating versatility across different types of cyber attacks. Notably, our approach maintains a low computational overhead equating to an O(n) time complexity, making it suitable for real-time applications in critical water system infrastructure. Our work underscores the importance of modeling feature interactions in water systems, offering a robust tool for operators to diagnose and mitigate root cause attacks more effectively. I NTRODUCTION W ATER systems at the physical layer comprise critical components such as flow and pressure sensors, and actuators, which are monitored and controlled by cyber layer systems to ensure a safe and reliable water supply for both communities and industries.

For a robot with redundant sensors and actuators distributed throughout its body, it is difficult to construct a controller or a neural network using all of them due to computational cost and complexity. Therefore, it is effective to extract functionally related sensors and actuators, group them, and construct a controller or a network for each of these groups. In this study, the functional and spatial connections among sensors and actuators are embedded into a graph structure and a method for automatic grouping is developed. Taking a musculoskeletal humanoid with a large number of redundant muscles as an example, this method automatically divides all the muscles into regions such as the forearm, upper arm, scapula, neck, etc., which has been done by humans based on a geometric model. The functional relationship among the muscles and the spatial relationship of the neural connections are calculated without a geometric model.

This paper introduces Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms such as bees and ants are integrated with the computational power of Artificial Intelligence (AI). This interdisciplinary field seeks to create systems that are not only smart but also adaptive and responsive in ways that mimic the nature. As FI evolves, it holds the promise of revolutionizing the way we approach complex problems, leveraging the best of both biological and digital worlds to create solutions that are more effective, sustainable, and harmonious with the environment. We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily).

When a robot executes a task, it is necessary to model the relationship among its body, target objects, tools, and environment, and to control its body to realize the target state. However, it is difficult to model them using classical methods if the relationship is complex. In addition, when the relationship changes with time, it is necessary to deal with the temporal changes of the model. In this study, we have developed Deep Predictive Model with Parametric Bias (DPMPB) as a more human-like adaptive intelligence to deal with these modeling difficulties and temporal model changes. We categorize and summarize the theory of DPMPB and various task experiments on the actual robots, and discuss the effectiveness of DPMPB.

Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, transformer models and generative AI. SNNs have local learning rules, are able to learn on small data sets, and can adapt through neuromodulation. Although research has shown their advantages, there are still few compelling practical applications, especially at the edge where sensors and actuators need to be processed in a timely fashion. One reason for this might be that SNNs are much more challenging to understand, build, and operate due to their intrinsic properties. For instance, the mathematical foundation involves differential equations rather than basic activation functions. To address these challenges, we have developed CARLsim++. It is an integrated toolbox that enables fast and easy creation of neuromorphic applications. It encapsulates the mathematical intrinsics and low-level C++ programming by providing a graphical user interface for users who do not have a background in software engineering but still want to create neuromorphic models. Developers can easily configure inputs and outputs to devices and robots. These can be accurately simulated before deploying on physical devices. CARLsim++ can lead to rapid development of neuromorphic applications for simulation or edge processing.

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building.