This study presents a learning - based nonlinear algorithm for tracking control of differential - drive mobile robots. The Computed Torque Method (CTM) suffers from inaccurate knowledge of system parameters, while Deep Reinforcement Learning (DRL) algorithms a re known for sample inefficiency and weak stability guarantees. The proposed method replaces the black box policy network of a DRL agent with a gray box Computed Torque Controller (CTC) to improve sample efficiency and ensure closed loop stability. This ap proach enables finding an optimal set of controller parameters for an arbitrary reward function using only a few short learning episodes. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used for this purpose. Additionally, some controller parameters are constrained to lie within known value ranges, ensuring the RL agent learns physically plausible values. A technique is also ap plied to enforce a critically damped closed loop time response. The controller's performance is evaluated on a differential drive mobile robot simulated in the MuJoCo physics engine and compared against the raw CTC and a conventional kinematic controller.

Omnidirectional videos that capture the entire surroundings are employed in a variety of fields such as VR applications and remote sensing. However, their wide field of view often causes unwanted objects to appear in the videos. This problem can be addressed by video inpainting, which enables the natural removal of such objects while preserving both spatial and temporal consistency. Nevertheless, most existing methods assume processing ordinary videos with a narrow field of view and do not tackle the distortion in equirectangular projection of omnidirectional videos. To address this issue, this paper proposes a novel deep learning model for omnidirectional video inpainting, called Distortion-Aware Omnidirectional Video Inpainting (DAOVI). DAOVI introduces a module that evaluates temporal motion information in the image space considering geodesic distance, as well as a depth-aware feature propagation module in the feature space that is designed to address the geometric distortion inherent to omnidirectional videos. The experimental results demonstrate that our proposed method outperforms existing methods both quantitatively and qualitatively.

Robotic manipulation in unstructured environments requires systems that can generalize across diverse tasks while maintaining robust and reliable performance. We introduce {GVF-TAPE}, a closed-loop framework that combines generative visual foresight with task-agnostic pose estimation to enable scalable robotic manipulation. GVF-TAPE employs a generative video model to predict future RGB-D frames from a single side-view RGB image and a task description, offering visual plans that guide robot actions. A decoupled pose estimation model then extracts end-effector poses from the predicted frames, translating them into executable commands via low-level controllers. By iteratively integrating video foresight and pose estimation in a closed loop, GVF-TAPE achieves real-time, adaptive manipulation across a broad range of tasks. Extensive experiments in both simulation and real-world settings demonstrate that our approach reduces reliance on task-specific action data and generalizes effectively, providing a practical and scalable solution for intelligent robotic systems.

The optimal power flow (OPF) is a large-scale optimization problem that is central in the operation of electric power systems. Although it can be posed as a nonconvex quadratically constrained quadratic program, the complexity of modern-day power grids raises scalability and optimality challenges. In this context, this work proposes a variational quantum paradigm for solving the OPF. We encode primal variables through the state of a parameterized quantum circuit (PQC), and dual variables through the probability mass function associated with a second PQC. The Lagrangian function can thus be expressed as scaled expectations of quantum observables. An OPF solution can be found by minimizing/maximizing the Lagrangian over the parameters of the first/second PQC. We pursue saddle points of the Lagrangian in a hybrid fashion. Gradients of the Lagrangian are estimated using the two PQCs, while PQC parameters are updated classically using a primal-dual method. We propose permuting primal variables so that OPF observables are expressed in a banded form, allowing them to be measured efficiently. Numerical tests on the IEEE 57-node power system using Pennylane's simulator corroborate that the proposed doubly variational quantum framework can find high-quality OPF solutions. Although showcased for the OPF, this framework features a broader scope, including conic programs with numerous variables and constraints, problems defined over sparse graphs, and training quantum machine learning models to satisfy constraints.

Traditional aggregate sorting methods, whether manual or mechanical, often suffer from low precision, limited flexibility, and poor adaptability to diverse material properties such as size, shape, and lithology. To address these limitations, this study presents a computer vision-aided robotic arm system designed for autonomous aggregate sorting in construction and mining applications. The system integrates a six-degree-of-freedom robotic arm, a binocular stereo camera for 3D perception, and a ROS-based control framework. Core techniques include an attention-augmented YOLOv8 model for aggregate detection, stereo matching for 3D localization, Denavit-Hartenberg kinematic modeling for arm motion control, minimum enclosing rectangle analysis for size estimation, and hand-eye calibration for precise coordinate alignment. Experimental validation with four aggregate types achieved an average grasping and sorting success rate of 97.5%, with comparable classification accuracy. Remaining challenges include the reliable handling of small aggregates and texture-based misclassification. Overall, the proposed system demonstrates significant potential to enhance productivity, reduce operational costs, and improve safety in aggregate handling, while providing a scalable framework for advancing smart automation in construction, mining, and recycling industries.

Reduced-order models (ROMs) provide a powerful means of synthesizing dynamic walking gaits on legged robots. Yet this approach lacks the formal guarantees enjoyed by methods that utilize the full-order model (FOM) for gait synthesis, e.g., hybrid zero dynamics. This paper aims to unify these approaches through a layered control perspective. In particular, we establish conditions on when a ROM of locomotion yields stable walking on the full-order hybrid dynamics. To achieve this result, given an ROM we synthesize a zero dynamics manifold encoding the behavior of the ROM -- controllers can be synthesized that drive the FOM to this surface, yielding hybrid zero dynamics. We prove that a stable periodic orbit in the ROM implies an input-to-state stable periodic orbit of the FOM's hybrid zero dynamics, and hence the FOM dynamics. This result is demonstrated in simulation on a linear inverted pendulum ROM and a 5-link planar walking FOM.

Long-range time series forecasting remains challenging, as it requires capturing non-stationary and multi-scale temporal dependencies while maintaining noise robustness, efficiency, and stability. Transformer-based architectures such as Autoformer and Informer improve generalization but suffer from quadratic complexity and degraded performance on very long time horizons. State space models, notably S-Mamba, provide linear-time updates but often face unstable training dynamics, sensitivity to initialization, and limited robustness for multivariate forecasting. To address such challenges, we propose the Quantum-Optimized Selective State Space Model (Q-SSM), a hybrid quantum-optimized approach that integrates state space dynamics with a variational quantum gate. Instead of relying on expensive attention mechanisms, Q-SSM employs a simple parametrized quantum circuit (RY-RX ansatz) whose expectation values regulate memory updates adaptively. This quantum gating mechanism improves convergence stability, enhances the modeling of long-term dependencies, and provides a lightweight alternative to attention. We empirically validate Q-SSM on three widely used benchmarks, i.e., ETT, Traffic, and Exchange Rate. Results show that Q-SSM consistently improves over strong baselines (LSTM, TCN, Reformer), Transformer-based models, and S-Mamba. These findings demonstrate that variational quantum gating can address current limitations in long-range forecasting, leading to accurate and robust multivariate predictions.

As organizations face increasing pressure to understand their corporate and products' carbon footprints, artificial intelligence (AI)-assisted calculation systems for footprinting are proliferating, but with widely varying levels of rigor and transparency. Standards and guidance have not kept pace with the technology; evaluation datasets are nascent; and statistical approaches to uncertainty analysis are not yet practical to apply to scaled systems. We present a set of criteria to validate AI-assisted systems that calculate greenhouse gas (GHG) emissions for products and materials. We implement a three-step approach: (1) Identification of needs and constraints, (2) Draft criteria development and (3) Refinements through pilots. The process identifies three use cases of AI applications: Case 1 focuses on AI-assisted mapping to existing datasets for corporate GHG accounting and product hotspotting, automating repetitive manual tasks while maintaining mapping quality. Case 2 addresses AI systems that generate complete product models for corporate decision-making, which require comprehensive validation of both component tasks and end-to-end performance. We discuss the outlook for Case 3 applications, systems that generate standards-compliant models. We find that credible AI systems can be built and that they should be validated using system-level evaluations rather than line-item review, with metrics such as benchmark performance, indications of data quality and uncertainty, and transparent documentation. This approach may be used as a foundation for practitioners, auditors, and standards bodies to evaluate AI-assisted environmental assessment tools. By establishing evaluation criteria that balance scalability with credibility requirements, our approach contributes to the field's efforts to develop appropriate standards for AI-assisted carbon footprinting systems.

There is increased interest in applying Artificial Intelligence and Machine Learning (AI/ML) within the nuclear industry and nuclear engineering community. Effective implementation of AI/ML could offer benefits to the nuclear domain, including enhanced identification of anomalies, anticipation of system failures, and operational schedule optimization. However, limited work has been done to investigate the feasibility and applicability of AI/ML tools in a functioning nuclear reactor. Here, we go beyond the development of a single model and introduce a multi-layered AI/ML architecture that integrates both information technology and operational technology data streams to identify, characterize, and differentiate (i) among diverse cybersecurity events and (ii) between cyber events and other operational anomalies. Leveraging Purdue Universitys research reactor, PUR-1, we demonstrate this architecture through a representative use case that includes multiple concurrent false data injections and denial-of-service attacks of increasing complexity under realistic reactor conditions. The use case includes 14 system states (1 normal, 13 abnormal) and over 13.8 million multi-variate operational and information technology data points. The study demonstrated the capability of AI/ML to distinguish between normal, abnormal, and cybersecurity-related events, even under challenging conditions such as denial-of-service attacks. Combining operational and information technology data improved classification accuracy but posed challenges related to synchronization and collection during certain cyber events. While results indicate significant promise for AI/ML in nuclear cybersecurity, the findings also highlight the need for further refinement in handling complex event differentiation and multi-class architectures.

Traditional power grid infrastructure presents significant barriers to renewable energy integration and perpetuates energy access inequities, with low-income communities experiencing disproportionately longer power outages. This study develops a Markov Decision Process (MDP) framework to optimize renewable energy allocation while explicitly addressing social equity concerns in electricity distribution. The model incorporates budget constraints, energy demand variability, and social vulnerability indicators across eight major U.S. cities to evaluate policy alternatives for equitable clean energy transitions. Numerical experiments compare the MDP-based approach against baseline policies including random allocation, greedy renewable expansion, and expert heuristics. Results demonstrate that equity-focused optimization can achieve 32.9% renewable energy penetration while reducing underserved low-income populations by 55% compared to conventional approaches. The expert policy achieved the highest reward, while the Monte Carlo Tree Search baseline provided competitive performance with significantly lower budget utilization, demonstrating that fair distribution of clean energy resources is achievable without sacrificing overall system performance and providing ways for integrating social equity considerations with climate goals and inclusive access to clean power infrastructure.