Few-shot learning seeks to recognize novel classes from limited examples. Model-agnostic meta-learning (MAML), known for its simplicity and flexibility, learns an effective initialization for fast adaptation in data-scarce settings. However, MAML-based methods face challenges when there is a significant distributional shift between training and testing tasks, leading to inefficient learning and poor generalization across domains. In this work, we identify the core issues: inflexible weight update rules and limited adaptive learning capabilities. Instead of focusing solely on better initialization, we aim to enhance the adaptation process. Consequently, we propose a novel Layer-Adaptive Proportional-Integral-Derivative (LA-PID) optimizer integrated into a meta-learning framework. This design incorporates classical control theory, utilizing PID control to dynamically adjust task-specific gains at each network layer. Additionally, the theoretical conditions for optimal hyperparameter initialization and global model convergence are addressed from both control and optimization perspectives. Experiments on benchmark datasets show that LA-PID achieves state-of-the-art performance in few-shot classification, cross-domain, and regression tasks, while requiring fewer training steps.

The threat of adversarial attacks on artificial intelligence in uncrewed systems has raised significant concerns regarding security. At a finer granularity, target-level adversarial attacks in instance segmentation play a critical role. However, existing methods are limited to generating disordered pixel perturbations, making it difficult to precisely control the attack targets. To address this challenge, a novel controllable attack for instance segmentation (CAIS) is proposed. CAIS precisely manipulates foreground targets while avoiding misleading the background, thereby enhancing the stealthiness of the attack. Specifically, CAIS introduces a focal gradient enhancement method to guide the victim detector to focus on foreground regions, strengthening the gradient signals for adversarial attacks. In addition, we design an attention guided perturbation method, which reduces the interference of background information through adaptive foreground–background gradient separation. This article introduces a dual loop asynchronous optimization strategy, integrating the aforementioned methods into the CAIS attack. Extensive experiments demonstrate that CAIS achieves superior attack performance on victim instance regions while maintaining prediction consistency in nonvictim regions. Consequently, this advancement will inspire the design of adversarial threat detection and defense mechanisms, ultimately contributing to the safety and reliability of uncrewed systems.

Flapping-wing robots exhibit numerous advantages in flight performance, which mimic the natural flight of birds or insects. However, autonomous takeoff remains a significant challenge for large-sized bird-like flapping-wing robots. To address this challenge, we design a jumping mechanism based on a bow-like carbon fiber spring. This mechanism is capable of repeated self-compression and release and can be readily integrated into flapping-wing robots to assist their jumping takeoff. Then we conduct a mechanical analysis of the motion states during the jumping takeoff process. Additionally, we propose a jump-flapping coupling control method based on sensor data to ensure a seamless transition and smooth coordination between the jumping and flapping action, thus enabling a smooth takeoff. Experimental results validate the effectiveness of the proposed jumping mechanism and its collaborative control strategy. This study provides support for advancing flapping-wing robots toward autonomous multi-modal locomotion and further deepens research in this field.

Adversarial attacks present a severe threat to the trustworthiness of remote sensing uncrewed systems. Existing detection methods are mostly limited to binary classification, lacking fine-grained traceability and relying heavily on adversarial example (AE) training. To overcome these challenges, we propose a training-free hierarchical adversarial attack traceability framework leveraging the zero-shot transfer ability of contrastive language-image pretraining (CLIP), eliminating the dependency on AE training. For the first time, we establish a multigranularity attack propagation path analysis system. Specifically, the framework leverages CLIP for fine-grained adversarial attack classification without fine-tuning, constructing a hierarchical traceability system (HTS) from coarse-grained to fine-grained semantic levels. Based on Bayesian inference, we design a hierarchical probability fusion method that improves coarse-grained results through maximum a posteriori (MAP) estimation of fine-grained classifiers, collaboratively optimizing hierarchical probability distributions. To capture frequency-domain characteristics of adversarial attacks, we propose a frequency-aware kernel approximation combining high-frequency-enhanced radial basis function (RBF) kernels with ridge regression, improving sensitivity to subtle perturbations in the reproducing kernel Hilbert space (RKHS). This study establishes a comprehensive traceability system for attack propagation chains, providing an interpretable, training-free paradigm for remote sensing security, significantly enhancing fine-grained detection capabilities of uncrewed systems. Extensive experiments on two public remote sensing datasets, RSSSN7 and DWEFS, and one proprietary dataset demonstrate that our method significantly improves adversarial classification accuracy across two victim models, achieving gains of 17.13%/18.89%/13.44% and 17.91%/19.58%/12.17%, respectively, compared with state-of-the-art (SOTA) training-free baselines.

Deep learning has demonstrated exceptional performance in fault diagnosis tasks that rely on large-scale datasets. However, the high cost of annotating such datasets has led to the emergence of various automatic annotation methods. While these methods reduce labeling costs, they inevitably introduce noisy labels, which pose significant challenges to the generalization and accuracy of deep learning-based diagnostic models. Although Learning with Noisy Labels (LNL) methods mitigate the adverse effects of noisy labels through strategies such as sample separation or label correction, many rely heavily on their own prediction results to guide subsequent training, which often introduces confirmation bias, limiting the effectiveness of the trained models. To address this limitation, this article draws inspiration from quantum collapse and proposes a novel LNL strategy named Multigranularity Labeling (MgL). By integrating observed labels, pseudolabels, and collapsed labels, MgL constructs the multigranularity labels, designed to suppress confirmation bias and improve the model's tolerance to noisy labels. Extensive experiments validate the effectiveness and superiority of MgL, particularly on training datasets with high noise intensity, such as those with 90% symmetric noise. This advancement offers promising opportunities for applying datasets with lower annotation costs in real-world scenarios, ultimately contributing to intelligent diagnostic systems.

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAMLbased approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-oftheart performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://github.com/yuguopin/LA-PID.

In the absence of a pre-built indoor map, robot navigation suffers from the limitations of sensors and environments, resulting in decreased efficiency in performing ad-hoc tasks. Given that blueprints are difficult to obtain, an intuitive method is to provide robots with prior knowledge via hand-drawn floor plans. However, due to the inability of robots to directly comprehend hand-drawn styles, the applicability of this method is limited. In this paper, we present a novel framework for hand-drawn floor plan reconstruction that can recognize abstract hand-drawn elements and standardize the reconstruction of hand-drawn floor plans, thereby providing robots with valuable global map information. Specifically, we design a new series of structure descriptors as reconstruction components and employ a deep learning-based model for recognition. Then the standardized results are obtained through the proposed floor plan reconstruction algorithm. To verify the effectiveness of the framework, we conduct experiments on electronic and paper hand-drawn floor plans. Compared with other state-of-the-art methods, our proposed method achieves superior reconstruction results. This work expands the application scenarios for indoor robots, enabling them to quickly comprehend the semantics of complex scenes, thereby enhancing the competitiveness in downstream tasks.

Unmanned aerial vehicles (UAVs) represent an essential component of advanced intelligent equipment that can be used as an aerial perception system by installing various sensors such as vision, hearing, touch, taste, and smell to achieve intelligently integrated perception of environments. However, these perception system with environmental information may be threatened by various internal and external attacks, causing a great challenge to the security of the UAV. The original security system relied on an expert knowledge base to prevent attacks, but the weaknesses of lacking proactivity and flexibility are gradually exposed. The strong resistance and survivability of biological systems can be used to fill this capability gap and provide new ideas for the security of the UAV perception system. Therefore, an endogenous security framework (ESUAV-NI) based on the neural system and immune system is proposed in this article. Through breeding artificial intelligence (AI) vaccines and distributed neural hierarchical control, we achieve the security protection for the UAV perception system. Moreover, we evaluated the AI vaccine breeding approach in the ESUAV-NI by conducting extensive experiments on internal threats and external aerial imagery camouflage data, respectively. The results show that the proposed approach has a superior performance for the UAV perception system.

For multi-agent area coverage path planning problem, existing researches regard it as a combination of Traveling Salesman Problem (TSP) and Coverage Path Planning (CPP). However, these approaches have disadvantages of poor observation ability in online phase and high computational cost in offline phase, making it difficult to be applied to energy-constrained Unmanned Aerial Vehicles (UAVs) and adjust strategy dynamically. In this paper, we decompose the task into two sub-problems: multi-agent path planning and sub-region CPP. We model the multi-agent path planning problem as a Collective Markov Decision Process (C-MDP), and design an Energy Constrained Multi-Agent Reinforcement Learning (ECMARL) algorithm based on the centralized training and distributed execution concept. Taking into account energy constraint of UAVs, the UAV propulsion power model is established to measure the energy consumption of UAVs, and load balancing strategy is applied to dynamically allocate target areas for each UAV. If the UAV is under energy-depleted situation, ECMARL can adjust the mission strategy in real time according to environmental information and energy storage conditions of other UAVs. When UAVs reach each sub-region of interest, Back-an-Forth Paths (BFPs) are adopted to solve CPP problem, which can ensure full coverage, optimality and complexity of the sub-problem. Comprehensive theoretical analysis and experiments demonstrate that ECMARL is superior to the traditional offline TSP-CPP strategy in terms of solution quality and computational time, and can effectively deal with the energy-constrained UAVs.