Endovascular interventions require precise, cooperative control of multiple instruments, such as guidewires and catheters, to navigate complex vascular anatomies. Current robotic systems, reliant on leader-follower control, depend heavily on operator expertise and lack intelligence. Learning-based methods, often limited to single-instrument control, fall short in complex clinical scenarios requiring multi-instrument coordination. This study proposes a Multi-Agent Fuzzy Reinforcement Learning (MAFRL) framework, guided by large language models (LLMs), for task-level autonomous, cooperative navigation in endovascular robotics. LLMs provide procedural priors and context-aware policy guidance, enabling adaptive decision-making for collaborative guidewire and catheter agents. Central to the framework, fuzzy reinforcement learning mitigates LLM-induced uncertainties by adaptively embedding clinical constraints into reward functions, ensuring strict adherence to procedural safety and precise alignment with the complexities of real-world endovascular interventions. Validated in a 3D vascular simulation, this approach achieves superior navigation performance and procedural efficiency compared to conventional methods, underscoring the transformative potential of fuzzy reinforcement learning in advancing LLM-guided MARL for endovascular robotics.

This paper presents a novel multi-modal deep learning framework for industrial robot joint health assessment and prediction, leveraging non-invasive signal fusion and Bayesian optimization. The proposed method addresses the challenges of comprehensive joint state monitoring in complex industrial environments without disrupting normal operations. We integrate Hall-effect current sensors, external accelerometers, and joint encoders to collect multi-modal data, including motor currents, vibrations, and kinematic information. A novel Adaptive Multi-Receptive Field Attention Network (AMRFAN) is employed to extract features from each modality, while a synchrosqueezing transform (SST) is utilized to capture time-frequency characteristics. An attention mechanism dynamically adjusts the weights of different modalities, and a bidirectional long short-term memory (BiLSTM) network models the temporal dependencies in the fused features. To enhance model performance and generalization, we implement a Bayesian optimization framework for hyperparameter tuning. Furthermore, we incorporate a Bayesian neural network to quantify prediction uncertainties, providing reliability metrics for decision-making processes. Experimental results on a four-axis industrial robot demonstrate that our framework achieves a 98.3% accuracy in joint health state classification and a mean absolute error of 4.2 in remaining useful life prediction, outperforming state-of-the-art single-modality methods. The proposed approach offers a robust, adaptable solution for real-time health monitoring and predictive maintenance of industrial robot joints, potentially improving manufacturing efficiency and reliability.

The control of locomotion in walking robots with various architectural designs presents significant challenges. While existing approaches primarily rely on low-level state information and isolated visual features, lacking the high-level semantic understanding that humans use to reason about movement and posture, we propose HeStIa, a novel framework that bridges visual perception, natural language understanding, and robotic control through multimodal learning. Our framework leverages multimodal large language models (MLLMs) to establish a semantic bridge between visual observations and motion control, enabling robots to understand and adjust their locomotion through both visual and linguistic modalities. By leveraging multimodal large language models (MLLMs), HeStIa establishes a semantic connection between visual observations and motion control, enabling robots to comprehend and adapt their locomotion through both visual and linguistic modalities. Our approach extracts spatiotemporal visual features from robot movements and transforms them into a cross-modal embedding space shared with textual descriptions. HeStIa incorporates an innovative vision-language-motion fusion mechanism to provide informed, context-aware feedback during the dynamic learning process. Through an asynchronous design, HeStIa effectively mitigates the inference delays typically associated with MLLMs while maintaining real-time performance in dynamic scenarios. The cross-modal representations learned by HeStIa facilitate more intuitive and efficient locomotion learning by grounding visual observations in natural language descriptions. Our comprehensive evaluation shows substantial improvements in motion naturalness, stability, and adaptability across diverse environmental conditions.

The advent of dual-polarization meteorological sensing systems has revolutionized our capacity to comprehend atmospheric precipitation dynamics through electromagnetic signal analysis. However, the intricate non-linear relationships within high-dimensional polarimetric signatures present formidable challenges in extracting actionable intelligence for meteorological multimedia applications. This manuscript presents HyperSpectral-M, a computational framework that enhances polarimetric signal interpretation through systematic manifold learning approaches in non-Euclidean spaces, enabling more precise analysis of complex atmospheric phenomena. The proposed HyperSpectral-M framework addresses the limitations of existing methods by incorporating two key innovations: a signal disentanglement mechanism and a physics-constrained reconstruction paradigm. The disentanglement mechanism employs quaternion-based geodesic flow mapping coupled with adaptive spectral decomposition to project polarimetric signatures onto lower-dimensional manifolds while preserving critical microphysical properties. This is augmented by a multi-scale differential geometry analyzer that captures intricate spatiotemporal correlations across varying atmospheric conditions. The reconstruction paradigm leverages adversarial manifold alignment with structured probabilistic inference to synthesize high-fidelity radar representations while maintaining electromagnetic consistency constraints. HyperSpectral-M demonstrates significant real-world impact on meteorological applications by improving precipitation nowcasting accuracy by 15-20% compared to operational methods, enabling more timely and accurate flood warnings. Field validation with emergency management agencies shows that reduces false alarm rates by 30-40% while increasing lead time for severe weather warnings by 15-30 minutes.

Over the past decade, significant advancements have been made in the research and industrialization of robotic systems for endovascular procedures, yet their clinical application remains relatively limited. Physicians commonly report that these robots lack certain intelligent assistive capabilities during procedures. There has been increasing interest and attempts to apply learning-centered algorithms to the training and enhancement of surgical robot skills. This paper proposes an autonomous navigation algorithm for interventional guidewires that is initially trained solely in a virtual simulation environment and subsequently deployed to a real-world robot. Experimental results demonstrate the feasibility of this approach for real-world applications. The proposed approach can help physicians reduce the learning curve for guidewire manipulation and elevate the robot to a higher level of autonomous operation, thereby breaking through the current bottleneck in the level of intelligence for clinical applications of interventional robots. It also holds promise for bringing intelligent transformation to future interventional procedures. Note to Practitioners—This work is motivated by the emerging need to increase the level of autonomy in robotic-assisted endovascular procedures, which has the potential to improve procedural efficiency, standardize procedures, and broaden the adoption of robotic systems in clinical practice. The proposed simulation-based reinforcement learning provides a safe and efficient method for training robotic systems, enabling them to master complex tasks in simulation environments prior to real-world application. The successful deployment of models trained in simulation onto physical robotic platforms demonstrates the feasibility of this method for real-world applications. The proposed simulation-based reinforcement learning method offers a promising and viable pathway for enhancing skill acquisition in endovascular interventional robots.

Sleep disorders have a significant impact on individuals’ health and overall quality of life. Among the most prevalent sleep disorders, obstructive sleep apnea and snoring necessitate effective monitoring and assessment methods. This paper introduces a novel approach to extract vital sign data, including heart rate, respiration rate, and body motion, from electronic signal graphs using wavelet analysis. In order to enhance the accuracy and efficiency of vital sign prediction, we employ reinforcement learning techniques to acquire an optimal policy for processing electronic signals. By identifying distinct features that characterize subjects with similar conditions, we enable personalized treatment approaches, ultimately leading to improvements in the overall health of individuals affected by sleep disorders.

Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods struggle to account for the intricate dependencies between joints. We introduce CoordiGraph, a novel architecture that leverages subequivariant principles from physics to enhance coordination of motion control with reinforcement learning. This method embeds the principles of equivariance as inherent patterns in the learning process under gravity influence, which aids in modeling the nuanced relationships between joints vital for motion control. Through extensive experimentation with sophisticated agents in diverse environments, we highlight the merits of our approach. Compared to current leading methods, CoordiGraph notably enhances generalization and sample efficiency.