BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation

High-quality data collection is a fundamental cornerstone for training humanoid whole-body visuomotor policies. Current data acquisition paradigms predominantly rely on robot teleoperation, which is often hindered by limited hardware accessibility and low operational efficiency. Inspired by the Universal Manipulation Interface (UMI), we propose BifrostUMI, a portable, efficient, and robot-free data collection framework tailored for humanoid robots. BifrostUMI leverages lightweight VR devices to capture human demonstrations as sparse keypoint trajectories while simultaneously recording wrist-mounted visual data. These multimodal data are subsequently utilized to train a high-level policy network that predicts future keypoint trajectories conditioned on the captured visual features. Through a robust keypoint retargeting pipeline, keypoint trajectories are precisely mapped onto the robot's morphology and executed via a whole-body controller. This approach enables the seamless transfer of diverse and agile behaviors from natural human demonstrations to humanoid embodiments. We demonstrate the efficacy and versatility of the proposed framework across two distinct experimental scenarios.

DIRCR: Dual-Inference Rule-Contrastive Reasoning for Solving RAVENs

Abstract visual reasoning remains challenging as existing methods often prioritize either global context or local row-wise relations, failing to integrate both, and lack intermediate feature constraints, leading to incomplete rule capture and entangled representations. To address these issues, we propose the Dual-Inference Rule-Contrastive Reasoning (DIRCR) model. Its core component, the Dual-Inference Reasoning Module, combines a local path for row-wise analogical reasoning and a global path for holistic inference, integrated via a gated attention mechanism. Additionally, a Rule-Contrastive Learning Module introduces pseudo-labels to construct positive and negative rule samples, applying contrastive learning to enhance feature separability and promote abstract, transferable rule learning. Experimental results on three RAVEN datasets demonstrate that DIRCR significantly enhances reasoning robustness and generalization. Codes are available at https://github.com/csZack-Zhang/DIRCR.

OmniUMI: Towards Physically Grounded Robot Learning via Human-Aligned Multimodal Interaction

UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physical interaction signals. This becomes a fundamental limitation in contact-rich manipulation, where success depends on contact dynamics such as tactile interaction, internal grasping force, and external interaction wrench that are difficult to infer from vision alone. We present OmniUMI, a unified framework for physically grounded robot learning via human-aligned multimodal interaction. OmniUMI synchronously captures RGB, depth, trajectory, tactile sensing, internal grasping force, and external interaction wrench within a compact handheld system, while maintaining collection--deployment consistency through a shared embodiment design. To support human-aligned demonstration, OmniUMI provides dual-force feedback through bilateral gripper feedback and natural perception of external interaction wrench in the handheld embodiment. Built on this interface, we extend diffusion policy with visual, tactile, and force-related observations, and deploy the learned policy through impedance-based execution for unified regulation of motion and contact behavior. Experiments demonstrate reliable sensing and strong downstream performance on force-sensitive pick-and-place, interactive surface erasing, and tactile-informed selective release. Overall, OmniUMI combines physically grounded multimodal data acquisition with human-aligned interaction, providing a scalable foundation for learning contact-rich manipulation.

RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection

Effective detection of unknown network security threats in multi-class imbalanced environments is critical for maintaining cyberspace security. Current methods focus on learning class representations but face challenges with unknown threat detection, class imbalance, and lack of interpretability, limiting their practical use. To address this, we propose RPM-Net, a novel framework that introduces reciprocal point mechanism to learn "non-class" representations for each known attack category, coupled with adversarial margin constraints that provide geometric interpretability for unknown threat detection. RPM-Net++ further enhances performance through Fisher discriminant regularization. Experimental results show that RPM-Net achieves superior performance across multiple metrics including F1-score, AUROC, and AUPR-OUT, significantly outperforming existing methods and offering practical value for real-world network security applications. Our code is available at:https://github.com/chiachen-chang/RPM-Net

Low-Resource Crop Classification from Multi-Spectral Time Series Using Lossless Compressors

Deep learning has significantly improved the accuracy of crop classification using multispectral temporal data. However, these models have complex structures with numerous parameters, requiring large amounts of data and costly training. In low-resource situations with fewer labeled samples, deep learning models perform poorly due to insufficient data. Conversely, compressors are data-type agnostic, and non-parametric methods do not bring underlying assumptions. Inspired by this insight, we propose a non-training alternative to deep learning models, aiming to address these situations. Specifically, the Symbolic Representation Module is proposed to convert the reflectivity into symbolic representations. The symbolic representations are then cross-transformed in both the channel and time dimensions to generate symbolic embeddings. Next, the Multi-scale Normalised Compression Distance (MNCD) is designed to measure the correlation between any two symbolic embeddings. Finally, based on the MNCDs, high quality crop classification can be achieved using only a k-nearest-neighbor classifier kNN. The entire framework is ready-to-use and lightweight. Without any training, it outperformed, on average, 7 advanced deep learning models trained at scale on three benchmark datasets. It also outperforms more than half of these models in the few-shot setting with sparse crop labels. Therefore, the high performance and robustness of our non-training framework makes it truly applicable to real-world crop mapping. Codes are available at: https://github.com/qinfengsama/Compressor-Based-Crop-Mapping.

Point wise or Feature wise? Benchmark Comparison of Public Available LiDAR Odometry Algorithms in Urban Canyons

Robust and precise localization is essential for the autonomous system with navigation requirements. Light detection and ranging (LiDAR) odometry is extensively studied in the past decades to achieve this goal. Satisfactory accuracy can be achieved in scenarios with abundant environmental features using existing LiDAR odometry (LO) algorithms. Unfortunately, the performance of the LiDAR odometry is significantly degraded in urban canyons with numerous dynamic objects and complex environmental structures. Meanwhile, it is still not clear from the existing literature which LO algorithms perform well in such challenging environments. To fill this gap, this paper evaluates an array of popular and extensively studied LO pipelines using the datasets collected in urban canyons of Hong Kong. We present the results in terms of their positioning accuracy and computational efficiency. Three major factors dominating the performance of LO in urban canyons are concluded, including the ego-vehicle dynamic, moving objects, and degree of urbanization. According to our experiment results, point-wise achieves better accuracy in urban canyons while feature-wise achieves cost-efficiency and satisfactory positioning accuracy.