arXiv subjects

Xiao Liu

Publications and source records attributed to Xiao Liu.

At least 163 records · Page 9Linked to original sources

arXiv2025

Galaxea Open-World Dataset and G0 Dual-System VLA Model

We present Galaxea Open-World Dataset, a large-scale, diverse collection of robot behaviors recorded in authentic human living and working environments. All demonstrations are gathered using a consistent robotic embodiment, paired with precise subtask-level language annotations to facilitate both training and evaluation. Building on this dataset, we introduce G0, a dual-system framework that couples a Vision-Language Model (VLM) for multimodal planning with a Vision-Language-Action (VLA) model for fine-grained execution. G0 is trained using a three-stage curriculum: cross-embodiment pre-training, single-embodiment pre-training, and task-specific post-training. A comprehensive benchmark spanning tabletop manipulation, few-shot learning, and long-horizon mobile manipulation, demonstrates the effectiveness of our approach. In particular, we find that the single-embodiment pre-training stage, together with the Galaxea Open-World Dataset, plays a critical role in achieving strong performance.

cs.RO↗

arXiv2025

Remote Quantum Networks based on Quantum Memories

Quantum networks, capable of transmitting arbitrary quantum states, provide a foundation for a wide range of quantum applications, including distributed quantum computing, distributed quantum sensing, and quantum communication. Photons are the natural carrier of information in quantum networks, but the exponential loss of optical fiber channels prevents the construction of large-scale quantum networks. A potential solution is implementing quantum repeaters based on quantum memories, which can efficiently establish long-distance entanglement from short-distance entanglement. In the past decades, intense efforts have been devoted to constructing large-scale quantum networks based on various atomic quantum memories. In this Perspective, we present a concise overview of current advancements in remote quantum networks, elucidate the imminent challenges that must be addressed, and discuss the future directions.

quant-ph↗

arXiv2025

Investigating VR Accessibility Reviews for Users with Disabilities: A Qualitative Analysis

Accessibility reviews provide valuable insights into both the limitations and benefits experienced by users with disabilities when using virtual reality (VR) applications. However, a comprehensive investigation into VR accessibility for users with disabilities is still lacking. To fill this gap, this study analyzes user reviews from the Meta and Steam stores of VR apps, focusing on the reported issues affecting users with disabilities. We applied selection criteria to 1,367,419 reviews from the top 40, the 20 most popular, and the 40 lowest-rated VR applications on both platforms. In total, 1,076 (0.078%) VR accessibility reviews referenced various disabilities across 100 VR applications. These applications were categorized into Action, Sports, Social, Puzzle, Horror, and Simulation, with Action receiving the highest number of accessibility related-reviews. We identified 16 different types of disabilities across six categories. Furthermore, we examined the causes of accessibility issues as reported by users with disabilities. Overall, VR accessibility reviews were predominantly under-supported.

cs.SE↗

arXiv2025

Combinatorial Development of Amorphous/nanocrystalline Biphase Soft Magnetic Alloys with Silicon-steel like Saturated Magnetic Induction

Maximization saturation magnetic induction (Bs) of soft magnetic alloys is essential for the high power-density electromagnetic devices. However, identifying the alloy compositions with high Bs often replies on the lab-intensive melt casting method and a high-throughput characterization on magnetic properties remains challenging. Here, we develop a new combinatorial method for fast screening alloys with optimal soft magnetic properties based on the high-throughput MOKE screening method. Based on the combinatorial method, we found that the alloys with a combination of high Bs and low coercivity (Hc) tend to have a feature of amorphous-nanocrystalline biphase microstructure. We also identified an amorphous/nanocrystalline alloy film with the composition the Fe68.09Co17.02B10.9Si4, exhibiting an ultra-high Bs up to 2.02 T that surpasses all amorphous/nanocrystalline alloys reported so far and is comparable to that of silicon steels, together with a high resistivity of 882 μΩ {\dot} cm, about 17 times of silicon steels. Our high-throughput magnetic screening method provides a paradigm for understanding the relationship between microstructure and magnetic properties and the development of the next-generation soft magnetic materials.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Modeling and Dimensional Optimization of Legged Mechanisms for Construction Robot

With the rapid development of the construction industry, issues such as harsh working environments, high-intensity and high-risk tasks, and labor shortages have become increasingly prominent. This drives higher demands for construction robots in terms of low energy consumption, high mobility, and high load capacity. This paper focuses on the design and optimization of leg structures for construction robots, aiming to improve their dynamic performance, reduce energy consumption, and enhance load-bearing capabilities. Firstly, based on the leg configuration of ants in nature, we design a structure for the robot's leg. Secondly, we propose a novel structural optimization method. Using the Lagrangian approach, a dynamic model of the leg was established. Combining the dynamic model with the leg's motion trajectory, we formulated multiple dynamic evaluation metrics and conducted a comprehensive optimization study on the geometric parameters of each leg segment. The results show that the optimized leg structure reduces peak joint torques and energy consumption by over 20%. Finally, dynamic simulation experiments were conducted using ADAMS. The results demonstrate a significant reduction in the driving power of each joint after optimization, validating the effectiveness and rationality of the proposed strategy. This study provides a theoretical foundation and technical support for the design of heavy-load, high-performance construction robots.

cs.RO↗

arXiv2025

Dynamic Parameter Identification of a Curtain Wall Installation Robotic Arm

In the construction industry, traditional methods fail to meet the modern demands for efficiency and quality. The curtain wall installation is a critical component of construction projects. We design a hydraulically driven robotic arm for curtain wall installation and a dynamic parameter identification method. We establish a Denavit-Hartenberg (D-H) model based on measured robotic arm structural parameters and integrate hydraulic cylinder dynamics to construct a composite parametric system driven by a Stribeck friction model. By designing high-signal-to-noise ratio displacement excitation signals for hydraulic cylinders and combining Fourier series to construct optimal excitation trajectories that satisfy joint constraints, this method effectively excites the characteristics of each parameter in the minimal parameter set of the dynamic model of the robotic arm. On this basis, a hierarchical progressive parameter identification strategy is proposed: least squares estimation is employed to separately identify and jointly calibrate the dynamic parameters of both the hydraulic cylinder and the robotic arm, yielding Stribeck model curves for each joint. Experimental validation on a robotic arm platform demonstrates residual standard deviations below 0.4 Nm between theoretical and measured joint torques, confirming high-precision dynamic parameter identification for the hydraulic-driven curtain wall installation robotic arm. This significantly contributes to enhancing the intelligence level of curtain wall installation operations.

cs.RO↗

arXiv2025

Multi-Objective Trajectory Planning for a Robotic Arm in Curtain Wall Installation

In the context of labor shortages and rising costs, construction robots are regarded as the key to revolutionizing traditional construction methods and improving efficiency and quality in the construction industry. In order to ensure that construction robots can perform tasks efficiently and accurately in complex construction environments, traditional single-objective trajectory optimization methods are difficult to meet the complex requirements of the changing construction environment. Therefore, we propose a multi-objective trajectory optimization for the robotic arm used in the curtain wall installation. First, we design a robotic arm for curtain wall installation, integrating serial, parallel, and folding arm elements, while considering its physical properties and motion characteristics. In addition, this paper proposes an NSGA-III-FO algorithm (NSGA-III with Focused Operator, NSGA-III-FO) that incorporates a focus operator screening mechanism to accelerate the convergence of the algorithm towards the Pareto front, thereby effectively balancing the multi-objective constraints of construction robots. The proposed algorithm is tested against NSGA-III, MOEA/D, and MSOPS-II in ten consecutive trials on the DTLZ3 and WFG3 test functions, showing significantly better convergence efficiency than the other algorithms. Finally, we conduct two sets of experiments on the designed robotic arm platform, which confirm the efficiency and practicality of the NSGA-III-FO algorithm in solving multi-objective trajectory planning problems for curtain wall installation tasks.

cs.RO↗

arXiv2025

Learning Patient-Specific Spatial Biomarker Dynamics via Operator Learning for Alzheimer's Disease Progression

Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response. Despite recent therapeutic advances, predictive models capable of accurately forecasting individualized disease trajectories remain limited. Here, we present a machine learning-based operator learning framework for personalized modeling of AD progression, integrating longitudinal multimodal imaging, biomarker, and clinical data. Unlike conventional models with prespecified dynamics, our approach directly learns patient-specific disease operators governing the spatiotemporal evolution of amyloid, tau, and neurodegeneration biomarkers. Using Laplacian eigenfunction bases, we construct geometry-aware neural operators capable of capturing complex brain dynamics. Embedded within a digital twin paradigm, the framework enables individualized predictions, simulation of therapeutic interventions, and in silico clinical trials. Applied to AD clinical data, our method achieves high prediction accuracy exceeding 90% across multiple biomarkers, substantially outperforming existing approaches. This work offers a scalable, interpretable platform for precision modeling and personalized therapeutic optimization in neurodegenerative diseases.

cs.LG↗

arXiv2025

Trajectory Planning of a Curtain Wall Installation Robot Based on Biomimetic Mechanisms

As the robotics market rapidly evolves, energy consumption has become a critical issue, particularly restricting the application of construction robots. To tackle this challenge, our study innovatively draws inspiration from the mechanics of human upper limb movements during weight lifting, proposing a bio-inspired trajectory planning framework that incorporates human energy conversion principles. By collecting motion trajectories and electromyography (EMG) signals during dumbbell curls, we construct an anthropomorphic trajectory planning that integrates human force exertion patterns and energy consumption patterns. Utilizing the Particle Swarm Optimization (PSO) algorithm, we achieve dynamic load distribution for robotic arm trajectory planning based on human-like movement features. In practical application, these bio-inspired movement characteristics are applied to curtain wall installation tasks, validating the correctness and superiority of our trajectory planning method. Simulation results demonstrate a 48.4% reduction in energy consumption through intelligent conversion between kinetic and potential energy. This approach provides new insights and theoretical support for optimizing energy use in curtain wall installation robots during actual handling tasks.

cs.RO↗

arXiv2025

Design and Dimensional Optimization of Legged Structures for Construction Robots

Faced with complex and unstructured construction environments, wheeled and tracked robots exhibit significant limitations in terrain adaptability and flexibility, making it difficult to meet the requirements of autonomous operation. Inspired by ants in nature, this paper proposes a leg configuration design and optimization method tailored for construction scenarios, aiming to enhance the autonomous mobility of construction robots. This paper analyzes the full operational motion performance of the leg during both swing and stance phases. First, based on kinematic modeling and multi-dimensional workspace analysis, the concept of an "improved workspace" is introduced, and graphical methods are used to optimize the leg dimensions during the swing phase. Furthermore, a new concept of "average manipulability" is introduced based on the velocity Jacobian matrix, and numerical solutions are applied to obtain the leg segment ratio that maximizes manipulability. To overcome the difficulties associated with traditional analytical methods, virtual prototype simulations are conducted in ADAMS to explore the relationship between the robot body's optimal flexibility and leg segment proportions. In summary, the leg segment proportions with the best comprehensive motion performance are obtained. This study presents the first multi-dimensional quantitative evaluation framework for leg motion performance tailored for construction environments, providing a structural design foundation for legged construction robots to achieve autonomous mobility in complex terrains.

cs.RO↗

arXiv2025

Topology Optimization of Leg Structures for Construction Robots Based on Variable Density Method

In complex terrain construction environments, there are high demands for robots to achieve both high payload capacity and mobility flexibility. As the key load-bearing component, the optimization of robotic leg structures is of particular importance. Therefore, this study focuses on the optimization of leg structures for construction robots, proposing a topology optimization strategy based on the SIMP (Solid Isotropic Microstructures with Penalization) variable density method along with a structural re-design approach. The design performance is comprehensively validated through finite element analysis using ANSYS. First, static and modal analyses are conducted to evaluate the rationality of the initial design. Then, topology optimization using the SIMP-based variable density method is applied to the femur section, which accounts for the largest proportion of the leg's weight. Based on iterative calculations, the femur undergoes secondary structural reconstruction. After optimization, the mass of the femur is reduced by 19.45\%, and the overall leg mass decreases by 7.92\%, achieving the goal of lightweight design. Finally, static and modal analyses are conducted on the reconstructed leg. The results demonstrate that the optimized leg still meets structural performance requirements, validating the feasibility of lightweight design. This research provides robust theoretical and technical support for lightweight construction robot design and lays a foundation for their efficient operation in complex construction environments.

cs.RO↗

arXiv2025

Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery

In this paper, we investigate a practical yet challenging task: On-the-fly Category Discovery (OCD). This task focuses on the online identification of newly arriving stream data that may belong to both known and unknown categories, utilizing the category knowledge from only labeled data. Existing OCD methods are devoted to fully mining transferable knowledge from only labeled data. However, the transferability learned by these methods is limited because the knowledge contained in known categories is often insufficient, especially when few annotated data/categories are available in fine-grained recognition. To mitigate this limitation, we propose a diffusion-based OCD framework, dubbed DiffGRE, which integrates Generation, Refinement, and Encoding in a multi-stage fashion. Specifically, we first design an attribute-composition generation method based on cross-image interpolation in the diffusion latent space to synthesize novel samples. Then, we propose a diversity-driven refinement approach to select the synthesized images that differ from known categories for subsequent OCD model training. Finally, we leverage a semi-supervised leader encoding to inject additional category knowledge contained in synthesized data into the OCD models, which can benefit the discovery of both known and unknown categories during the on-the-fly inference process. Extensive experiments demonstrate the superiority of our DiffGRE over previous methods on six fine-grained datasets.

cs.CV↗

arXiv2025

AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation

The rapid advancement of AI-generated video models has created a pressing need for robust and interpretable evaluation frameworks. Existing metrics are limited to producing numerical scores without explanatory comments, resulting in low interpretability and human evaluation alignment. To address those challenges, we introduce AIGVE-MACS, a unified model for AI-Generated Video Evaluation(AIGVE), which can provide not only numerical scores but also multi-aspect language comment feedback in evaluating these generated videos. Central to our approach is AIGVE-BENCH 2, a large-scale benchmark comprising 2,500 AI-generated videos and 22,500 human-annotated detailed comments and numerical scores across nine critical evaluation aspects. Leveraging AIGVE-BENCH 2, AIGVE-MACS incorporates recent Vision-Language Models with a novel token-wise weighted loss and a dynamic frame sampling strategy to better align with human evaluators. Comprehensive experiments across supervised and zero-shot benchmarks demonstrate that AIGVE-MACS achieves state-of-the-art performance in both scoring correlation and comment quality, significantly outperforming prior baselines including GPT-4o and VideoScore. In addition, we further showcase a multi-agent refinement framework where feedback from AIGVE-MACS drives iterative improvements in video generation, leading to 53.5% quality enhancement. This work establishes a new paradigm for comprehensive, human-aligned evaluation of AI-generated videos. We release the AIGVE-BENCH 2 and AIGVE-MACS at https://huggingface.co/xiaoliux/AIGVE-MACS.

cs.CV↗

arXiv2025

Reimagining Parameter Space Exploration with Diffusion Models

Adapting neural networks to new tasks typically requires task-specific fine-tuning, which is time-consuming and reliant on labeled data. We explore a generative alternative that produces task-specific parameters directly from task identity, eliminating the need for task-specific training. To this end, we propose using diffusion models to learn the underlying structure of effective task-specific parameter space and synthesize parameters on demand. Once trained, the task-conditioned diffusion model can generate specialized weights directly from task identifiers. We evaluate this approach across three scenarios: generating parameters for a single seen task, for multiple seen tasks, and for entirely unseen tasks. Experiments show that diffusion models can generate accurate task-specific parameters and support multi-task interpolation when parameter subspaces are well-structured, but fail to generalize to unseen tasks, highlighting both the potential and limitations of this generative solution.

cs.LG↗

arXiv2025

TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge of performing efficient language reasoning--especially during inference with extremely long outputs--has drawn increasing attention from the research community. In this work, we propose a dynamic ratio-based training pipeline that does not rely on sophisticated data annotations or interpolation between multiple models. We continuously balance the weights between the model's System-1 and System-2 data to eliminate redundant reasoning processes while preserving the model's reasoning capability. We validate our approach across models on DeepSeek-R1-Distill-7B and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying difficulty levels. Our method significantly reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. Our code and data will be available soon.

cs.CL↗

arXiv2025

Spectral effects of radiating gases on the ignition in a multiswirl staged model combustor using full-spectrum k distribution method -- A Large Eddy Simulation Investigation

Radiative heat transfer has been proven to be important during the ignition process in gas turbine. Those radiating gases (CO2, H2O, CO) generated during combustion may display strong spectral, or nongray behavior, which is difficult to both characterize and calculate. In this work, both the full-spectrum k-distribution (FSK) and weighted-sum-of-gray-gases (WSGG) method, along with the Dynamic-thickened-flame (DTF) and Large-Eddy-Simulation (LES) methods, are used to analyze how spectral behavior affects the ignition process in an experimental gas turbine. Results show that radiation affects the ignition process by heating the relatively low temperature regions. Consequently, each ignition phase is differently affected by different spectral treatments. During the initial kernel phase, spectral properties have minimal influence on flame structures and the ignition delay time due to the negligible radiation and optically-thin scenario. However, during the flame growth phase, significant differences appear in the flame structure and the flame propagation speed among different spectral treatments. After the flame fill the combustor and during the stable combustion phase, differences in flame structures calculated by different models become less, but radiation still play an important role in combustion. Therefore, high-fidelity spectral models are recommended during the modelling of the ignition process in the gas turbine.

physics.flu-dyn↗

arXiv2025

Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification

The internet has become a hotspot for hate speech (HS), threatening societal harmony and individual well-being. While automatic detection methods perform well in identifying explicit hate speech (ex-HS), they struggle with more subtle forms, such as implicit hate speech (im-HS). We tackle this problem by introducing a new taxonomy for im-HS detection, defining six encoding strategies named codetypes. We present two methods for integrating codetypes into im-HS detection: 1) prompting large language models (LLMs) directly to classify sentences based on generated responses, and 2) using LLMs as encoders with codetypes embedded during the encoding process. Experiments show that the use of codetypes improves im-HS detection in both Chinese and English datasets, validating the effectiveness of our approach across different languages.

cs.CL↗

arXiv2025

EX-4D: EXtreme Viewpoint 4D Video Synthesis via Depth Watertight Mesh

Generating high-quality camera-controllable videos from monocular input is a challenging task, particularly under extreme viewpoint. Existing methods often struggle with geometric inconsistencies and occlusion artifacts in boundaries, leading to degraded visual quality. In this paper, we introduce EX-4D, a novel framework that addresses these challenges through a Depth Watertight Mesh representation. The representation serves as a robust geometric prior by explicitly modeling both visible and occluded regions, ensuring geometric consistency in extreme camera pose. To overcome the lack of paired multi-view datasets, we propose a simulated masking strategy that generates effective training data only from monocular videos. Additionally, a lightweight LoRA-based video diffusion adapter is employed to synthesize high-quality, physically consistent, and temporally coherent videos. Extensive experiments demonstrate that EX-4D outperforms state-of-the-art methods in terms of physical consistency and extreme-view quality, enabling practical 4D video generation.

cs.CV↗