Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning, details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain.

Fashion recommendation is crucial for consumers to express their self-image and personal style. To boost recommendation accuracy and rationality, researchers have explored state-of-the-art (SOTA) methods that incorporate items’ visual and textual information, along with their pairing records. However, these methods fail to fully explore how latent semantic-stylistic correlations between items influence recommendations, and inadequately tackle exposure bias, which results in skewed, restricted recommendations that prioritize popular or frequently observed outfits. To address these limitations, we propose DRFusionRec, a fashion recommender that enhances recommendation rationality and diversity by fully leveraging item semantic-stylistic correlations while mitigating exposure bias. Specifically, to harness rich item correlations, we introduce a novel Multi-Factor Relationship Measurement (MRM) matrix. It integrates semantic and stylistic features by mining the synergistic interaction probabilities across semantically and stylistically adjacent items, capturing the latent compatibility patterns. This matrix is then used to refine item features for richer details. To address homogeneity from exposure bias, we propose an Adaptive Propensity Score (APS) strategy. By dynamically weighting item popularity (direct influence) and popularity of style-similar neighbors (indirect contextual influence), we model exposure confounders to derive item propensity scores. Integrating these scores into item features effectively mitigates the bias. Lastly, MRM and APS synergistically optimize item representations for rationale-based and diverse recommendations. Experimental results validate that the DRFusionRec outperforms SOTA methods in capturing item compatibility, ensuring diverse recommendations, and maintaining reasonable complexity.

This paper presents HyperGraph ROS, an open-source robot operating system that unifies intra-process, inter-process, and cross-device computation into a computational hypergraph for efficient message passing and parallel execution. In order to optimize communication, HyperGraph ROS dynamically selects the optimal communication mechanism while maintaining a consistent API. For intra-process messages, Intel-TBB Flow Graph is used with C++ pointer passing, which ensures zero memory copying and instant delivery. Meanwhile, inter-process and cross-device communication seamlessly switch to ZeroMQ. When a node receives a message from any source, it is immediately activated and scheduled for parallel execution by Intel-TBB. The computational hypergraph consists of nodes represented by TBB flow graph nodes and edges formed by TBB pointer-based connections for intra-process communication, as well as ZeroMQ links for inter-process and cross-device communication. This structure enables seamless distributed parallelism. Additionally, HyperGraph ROS provides ROS-like utilities such as a parameter server, a coordinate transformation tree, and visualization tools. Evaluation in diverse robotic scenarios demonstrates significantly higher transmission and throughput efficiency compared to ROS 2. Our work is available at https://github.com/wujiazheng2020a/hyper_graph_ros.

The Diffusion model has a strong ability to generate wild images. However, the model can just generate inaccurate images with the guidance of text, which makes it very challenging to directly apply the text-guided generative model for virtual try-on scenarios. Taking images as guiding conditions of the diffusion model, this paper proposes a brand new personalized virtual try-on model (PE-VITON), which uses the two stages (shape control and texture guidance) to decouple the clothing attributes. Specifically, the proposed model adaptively matches the clothing to human body parts through the Shape Control Module (SCM) to mitigate the misalignment of the clothing and the human body parts. The semantic information of the input clothing is parsed by the Texture Guided Module (TGM), and the corresponding texture is generated by directional guidance. Therefore, this model can effectively solve the problems of weak reduction of clothing folds, poor generation effect under complex human posture, blurred edges of clothing, and unclear texture styles in traditional try-on methods. Meanwhile, the model can automatically enhance the generated clothing folds and textures according to the human posture, and improve the authenticity of the virtual try-on. In this paper, qualitative and quantitative experiments are carried out on high-resolution paired and unpaired datasets, the results show that the proposed model outperforms the state-of-the-art model.

Online shopping has experienced rapid development recently. However, compared to offline shopping, the return rate and complaint rate of online shopping are much higher, especially for online clothes shopping. In order to solve this problem, virtual fitting technology arises at the right moment, and 3D human modeling is a crucial part of virtual fitting technology. The reconstruction of the parametric 3D human models often faces challenges as long fitting time, low accuracy and fuzzy depth information. Although the reconstruction of nonparametric 3D human model has improved accuracy and detail to some extent, such models typically lack flexibility and controllability. Therefore, this paper reconstructs a high-precision parametric 3D human model by proposing a multi-view iterative registration strategy based on pose prior estimation, which is integrated with a nonparametric 3D human model based on implicit functions. The resulting model retains not only high-precision and detail but also high flexibility and controllability, which has achieved a good effect on the application of 3D virtual fitting. The proposed method is tested on the open-source 3D human body dataset multi-garment network (MGN). The Chamfer distance of the SMPL mannequin reconstructed from multiple views can reach 2.18[Formula: see text]cm and the per-vertical-error can reach 1.63[Formula: see text]cm.

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original non-compressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.

The purpose of this study is to discuss the influence of nonlinearity upon X-band radar observations. For simplicity, the analytical dispersion relationship of finite amplitude ocean wave theory was applied and discussed. We found that the shallow water dispersion relation curve covers more ocean wave energy than deep water and linear dispersion relationship. However, the shallow water dispersion relationship filter can not derive the ocean spectrum from radar image spectrum. The accurate measurement of ocean wave amplitude and water depth may be contributed to the results. More data are needed to analyze the reasons.

Polarimetric scattering mechanisms of ocean surface are important for improving ocean surface scattering model and detecting objects on the ocean surface, especially at large incidence angles and wave breaking. We proposed polarimetric parameters technique to analyze the scattering mechanisms of ocean surface. Lacks of the related data, we only analyzed the variances of polarimereic parameters with incidence angles for C, L and P-band at low ocean state. The results show the polarimetric parameters are different at different incidence angles and different radar work bands. It further indicates that the same ocean surface will present different scattering mechanisms at different radar work conditions.