The rapid evolution of 6G networks and AI-generated content (AIGC) is reshaping service provisioning at the wireless edge, where low-latency and computation-intensive tasks must be efficiently supported. Integrating AIGC with 6G Internet of Things (IoT) ecosystems offers significant benefits, enabling real-time analytics, IoT data augmentation and synthesis, personalized services, and intelligent resource coordination across heterogeneous devices. However, provisioning AIGC services at the mobile edge poses formidable challenges: massive data heterogeneity, stringent delay requirements, and the inherent limitations of learning-based offloading methods, such as high training cost, unstable convergence, and weak transparency. Inspired by the Quantum Approximate Optimization Algorithm (QAOA), we propose a hybrid optimization framework that combines classical wireless bandwidth pre-allocation for IoT devices connected to mobile edge computing (MEC) servers with Quadratic Unconstrained Binary Optimization (QUBO)-based AIGC server selection. This hybrid classical-quantum design ensures stable results and enables efficient exploration of combinatorial allocation spaces. We further implement the quantum circuits and evaluate the performance of our proposed approach. Simulation results show that our method achieves lower processing latency and greater stability than conventional reinforcement learning and heuristic baselines in small- to medium-scale IoT deployments. While current quantum hardware scalability remains a constraint, the framework points toward a promising pathway for large-scale AIGC offloading as quantum technology matures.

Garment manipulation is a significant challenge for robots due to the complex dynamics and potential self-occlusion of garments. Most existing methods of efficient garment unfolding overlook the crucial role of standardization of flattened garments, which could significantly simplify downstream tasks like folding, ironing, and packing. This paper presents APS-Net, a novel approach to garment manipulation that combines unfolding and standardization in a unified framework. APS-Net employs a dual-arm, multi-primitive policy with dynamic fling to quickly unfold crumpled garments and pick-and-place(p&p) for precise alignment. The purpose of garment standardization during unfolding involves not only maximizing surface coverage but also aligning the garment’s shape and orientation to predefined requirements. To guide effective robot learning, we introduce a novel factorized reward function for standardization, which incorporates garment coverage (Cov), keypoint distance (KD), and intersection-over-union (IoU) metrics. Additionally, we introduce a spatial action mask and an Action Optimized Module to improve unfolding efficiency by selecting actions and operation points effectively. In simulation, APS-Net outperforms state-of-the-art methods for long sleeves, achieving 3.9% better coverage, 5.2% higher IoU, and a 0.14 decrease in KD (7.09% relative reduction). Real-world folding tasks further demonstrate that standardization simplifies the folding process. Project page: https://hellohaia.github.io/APS/

Point cloud classification is critical for three-dimensional scene understanding. However, in real-world scenarios, depth cameras often capture partial, single-view point clouds of objects with different poses, making their accurate classification a challenge. In this paper, we propose a novel point cloud classification network that captures the detailed spatial structure of objects by constructing tetrahedra, which is different from point-wise operations. Specifically, we propose a RISpaNet block to extract rotation-invariant features. A rotation-invariant property generation module is designed in RISpaNet for constructing rotation-invariant tetrahedron properties (RITPs). Meanwhile, a multi-scale pooling module and a hybrid encoder are used to process RITPs to generate integrated rotation-invariant features. Further, for single-view point clouds, a complete point cloud auxiliary branch and a part-whole correlation module are jointly employed to obtain complete point cloud features from partial point clouds. Experimental results show that this network performs better than other state-of-the-art methods, evaluated on four public datasets. We achieved an overall accuracy of 94.7% (+2.0%) on ModelNet40, 93.4% (+5.9%) on MVP, 94.7% (+6.3%) on PCN and 94.8% (+1.7%) on ScanObjectNN. Our project website is https://luxurylf.github.io/RISpaNet_project/.

Robotic cloth manipulation poses significant challenges due to the fabric’s complex dynamics and the high dimensionality of configuration spaces. Previous approaches have focused on isolated smoothing or folding tasks and relied heavily on simulations, often struggling to bridge the sim-to-real gap. This gap arises as simulated cloth dynamics fail to capture real-world properties such as elasticity, friction, and occlusions, causing accuracy loss and limited generalization. To tackle these challenges, we propose a two-stream architecture with sequential and spatial pathways, unifying smoothing and folding tasks into a single adaptable policy model. The sequential stream determines pick-and-place positions, while the spatial stream, using a connectivity dynamics model, constructs a visibility graph from partial point cloud data, enabling the model to infer the cloth’s full configuration despite occlusions. To address the sim-to-real gap, we integrate real-world human demonstration data via a hand-tracking detection algorithm, enhancing real-world performance across diverse cloth configurations. Our method, validated on a UR5 robot across six distinct cloth folding tasks, consistently achieves desired folded states from arbitrary crumpled initial configurations, with success rates of 100.0%, 100.0%, 83.3%, 66.7%, 83.3%, and 66.7%. It outperforms state-of-the-art cloth manipulation techniques and generalizes to unseen fabrics with diverse colors, shapes, and stiffness. Project page: https://zcswdt.github.io/SSFold/.

Detecting earthquake-induced landslides in remote sensing images is challenging due to the varying sizes of landslides, uneven distribution, and the prevalence of small targets. This study proposes a novel approach, the TLSTMF-YOLO model, which combines a C3-Swin-Transformer and multiscale feature fusion techniques to enhance detection accuracy and efficiency. Key innovations include the use of a convolutional block attention module (CBAM) to improve feature representation, and a bidirectional feature pyramid network (BiFPN) for optimized cross-scale feature fusion. To address data scarcity, a transfer learning strategy is applied, supported by an AdamW optimizer and cosine learning rate strategy for faster convergence. Evaluations on the Jiuzhaigou and Luding landslide datasets demonstrate the model’s effectiveness, achieving precision, recall, and mean average precision (mAP)@0.5 of 95.7%, 89.9%, and 90.5% on the Jiuzhaigou dataset, and 96.0%, 90.9%, and 94.5% on the Luding dataset, respectively. In addition, the model processes frames efficiently, with times of 6.61 and 12.2 ms on the two datasets. These results confirm the model’s capability for accurate and efficient landslide detection, highlighting its potential for real-world applications.