70 accepted papers
Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first …
Active scene reconstruction aims to autonomously recover the fine-grained appearance and structural details of a complex unknown scenes. Existing approaches based on 2D topological or voxel-based abstractions often scale poorly to large environments and rely heavily on handcrafted features and heuri…
Foundation segmentation models, such as SAM and its video-oriented variant SAM2, have achieved remarkable success in natural image and video segmentation. However, their direct application to echocardiography video is challenged by structural uncertainty arising from severe speckle noise and blurry
Diffusion models are effective for waypoint prediction in visual navigation, but standard sampling and test time guidance can produce unsafe or inefficient trajectories when updates drift off the training manifold. We propose Fisher Preserving Guidance with Outer Product Span Projection, a training-…
Zero-shot anomaly detection (ZSAD) aims to identify unseen anomalies without abnormal supervision, which is essential for open-world scenarios. Recent vision-language models such as CLIP enable anomaly reasoning through shared visual-textual embeddings, but existing methods often rely on coarse prom
While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive scienc…
Reinforcement learning (RL) has renewed interest in end-to-end robot navigation, yet dynamic, crowd-like scenes remain difficult due to partial observability and the fragility of online tracking. We introduce GPTS-Nav, a Graph-Privileged Teacher-Student RL framework for LiDAR-only navigation. A grap
Forecasting Origin-Destination (OD) demand for new metro lines is critical for sustainable infrastructure planning but faces spatiotemporal out-of-distribution challenges. Existing models often struggle to capture heterogeneous interaction patterns in changing topologies and overlook inherent uncert
In this paper, we propose a rotation-constrained compensation method to address the errors introduced by structured pruning of large language models (LLMs). LLMs are trained on massive datasets and accumulate rich semantic knowledge in their representation space. In contrast, pruning is typically…
Pose-agnostic Anomaly Detection (PAD) aims to detect anomalies when the poses of query images are unknown and differ from those in the training set. Therefore, accurately estimating the camera poses for the query images in the test set is critical for this task. Existing query-specific framework met
Web agents require massive trajectories to generalize, yet real-world training is constrained by network latency, rate limits, and safety risks. We introduce \textbf{WebWorld} series, the first open-web simulator trained at scale. While existing simulators are restricted to closed environments with …
In this letter, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ArenaSim</i>, a novel simulation platform designed for realistic and efficient self-play learning in multi-robot cooperative-competitive games. Compared to previous simulati
Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image features in X-ray images that influence the outputs of report
Dielectric elastomer actuators (DEAs) face a performance tradeoff between achieving large displacements and high driving speeds, which limits their use in precision actuation scenarios requiring both rapid response and a wide motion range. To address these limitations, this study introduces a novel
Effectively integrating the complementary information from RGB-D images presents a significant challenge in robotic grasping. In this letter, we propose a full-flow bidirectional feature fusion grasp detection network (FFBGNet) based on a hybrid architecture to generate accurate grasp poses from RGB
Global seismic tomography, taking advantage of seismic waves from natural earthquakes, provides essential insights into the earth's internal dynamics. Advanced Full-Waveform Inversion (FWI) techniques, whose aim is to meticulously interpret every detail in seismograms, confront formidable computatio…
Recently, many humanoid robots have been increasingly deployed in various facilities, including hospitals and assisted living environments, where they are often remotely controlled by human operators. Their kinematic redundancy enhances reachability and manipulability, enabling them to navigate comp
Acoustic cameras have found many applications in practice. Accurate and reliable extrinsic calibration of the microphone array and visual sensors within acoustic cameras is crucial for fusing visual and auditory measurements. Existing calibration methods either require prior knowledge of the microph…
The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human expert…
Autonomous exploration in large-scale, unknown environments remains a significant challenge in mobile robotics. In this paper, we propose a scalable exploration framework that integrates heterogeneous topological representations, lightweight global-local graph reasoning, and reinforcement learning.
Lifelong Multi-Agent Path Finding (LMAPF) is vital for logistics and production as automation scales. Existing real-time methods rely on distributed, learning-based frameworks but often depend on search-based single-agent planners, creating time bottlenecks. They also use complex communication struc
3D Gaussian Splatting (3DGS) has been widely discussed due to its impressive ability to provide fast and high-fidelity reconstruction. However, the training efficiency of 3D Gaussian Splatting limits its application in real-time tasks such as Simultaneous Localization and Mapping (SLAM). In this pap…
Generative pre-trained transformers are exceedingly effective as generative models and classifiers, widely used in natural language processing and computer vision. This work contributes to the exploration of generative pre-trained transformer-based models in the proprietary protocol network traffic.…
There have been researches revealing obvious associations between facial phenotypes and non-communicable diseases (NCDs), which enables effective health assessment with the integration of model-based learning methods. However, the paucity and poor quality of available datasets hinder the development…
Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent…
The emergence of personalized generation has made it possible to create texts or images that meet the unique needs of users. Recent advances mainly focus on style or scene transfer based on given keywords. However, in e-commerce and recommender systems, it is almost an untouched area to explore user…
Despite large language models (LLMs) have demonstrated impressive performance in various tasks, they are still suffering from the factual inconsistency problem called hallucinations. For instance, LLMs occasionally generate content that diverges from source article, and prefer to extract information…
Collaborative perception allows for information sharing between multiple agents such as vehicles and infrastructure to obtain a comprehensive view of the environment through communication and fusion. Current research on multi-agent collaborative perception systems often assumes ideal communication a…
Instruction tuning is a crucial technique for aligning language models with humans’ actual goals in the real world. Extensive research has highlighted the quality of instruction data is essential for the success of this alignment. However, creating high-quality data manually is labor-intensive and t…
The diffusion model has demonstrated impressive performance in offline reinforcement learning. However, non-deterministic sampling in diffusion models can lead to unstable performance. Furthermore, the lack of confidence measurements makes it difficult to evaluate the reliability and trustworthiness…
In recent research, significant advancements have been achieved in tool learning for large language models. Looking towards future advanced studies, the issue of fully autonomous tool utilization is particularly intriguing: given only a query, language models can autonomously decide whether to emplo…
In recent advancements concerning Domain Adaptive Object Detection (DAOD), unsupervised domain adaptation techniques have proven instrumental. These methods enable enhanced detection capabilities within unlabeled target domains by mitigating distribution differences between source and target domains…
This paper presents a novel integration of a shared autonomous mobile humanoid robot for remote nursing assistance. The proposed nursing robot has a motorized versatile supporting structure to allow flexible integration of the system components, autonomously adjust its mobile manipulation workspace…
Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to make accurate predictions due to the existence of non-stationarity in real-world data, denoting the data distribution rap…
Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptability to unknown map sizes, and slow simulation speed. To speed up convergence, we combine curriculum learning (CL) with…
We introduce Back-Modality, a novel data augmentation schema predicated on modal transformation. Data from an initial modality undergoes transformation to an intermediate modality, followed by a reverse transformation. This framework serves dual roles. On one hand, it operates as a general data augm…
Developing cultural adaptation methods is important, which can improve the model performance on the low-resource ones and provide more equitable opportunities for everyone to benefit from advanced technology. Past methods primarily focused on multilingual and multimodal capabilities, and the improve…
When humans control or supervise remote robot manipulation, augmented reality (AR) visual cues overlaid on the remote camera video stream can effectively enhance human's remote perception of task and robot states, and comprehension of the robot autonomy's capability and intent. In this work, we cond…
Payload capacity is a crucial factor for climbing robots, as it directly affects their ability to carry and transport heavy loads during various climbing tasks. However, many dry adhesion-based legged robots prioritize foot design from a bionic perspective to accomplish various climbing tasks while
While motion classification architectures have improved in accuracy and robustness in recent years, computationally expensive approaches and sophisticated hardware dependencies limit their real-world applicability. To overcome these challenges, we have designed a lightweight, realtime architecture f…
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous methods have achieved promising results, they did not fully utilize the information of cross-view and intra-view. To furt…
Face Anti-Spoofing (FAS) is recently studied under the continual learning setting, where the FAS models are expected to evolve after encountering data from new domains. However, existing methods need extra replay buffers to store previous data for rehearsal, which becomes infeasible when previous da…
Test-time domain adaptation, i.e. adapting source-pretrained models to the test data on-the-fly in a source-free, unsupervised manner, is a highly practical yet very challenging task. Due to the domain gap between source and target data, inference quality on the target domain can drop drastically es…
This work compared human-robot shared and supervisory control of remote robots for dexterous manipulation, and examined how the reliability of robot autonomy affects human operator performance, workload, and preference for robot assistance. Specifically, we implemented two human-robot collaboration
Virtual try-on is an important technology supporting online apparel shopping, which provides consumers with a virtual experience to fit garments without physically wearing them. Recently, the image-based virtual try-on has received growing research attention. However, the synthetic results of existi…
Robot teleoperation via human motion tracking has been proven to be easy to learn, intuitive to operate, and facilitate faster task execution than existing baselines. However, precise control while performing the dexterous telemanipulation tasks is still a challenge. In this paper, we implement sens…
Motion tracking interfaces are intuitive for free-form teleoperation tasks. However, efficient manipulation control can be difficult with such interfaces because of issues like the interference of unintended motions and the limited precision of human motion control. The limitation in control efficie…
Tactile object recognition is vital for robotic handling systems; however, existing technologies that concentrate on tactile sensors with high modulus are not suitable for soft grippers to classify deformable objects. In this letter, we integrated an indenter layer into the traditional microfluidic
Computerized Adaptive Testing (CAT) refers to an efficient and personalized test mode in online education, aiming to accurately measure student proficiency level on the required subject/domain. The key component of CAT is the "adaptive" question selection algorithm, which automatically selects the b…
"Marker-less monocular 3D human motion capture (MoCap) with scene interactions is a challenging research topic relevant for extended reality, robotics and virtual avatar generation. Due to the inherent depth ambiguity of monocular settings, 3D motions captured with existing methods often contain sev…
The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on…
Perception and portability are critical issues for wearable gloves in hand assistive engineering. However, available wearable gloves either lack flexible sensing or are bulky. In this paper, we present a tendon-driven lightweight wearable glove with soft joint sensing, Sen-Glove. Sen-Glove is equipp…
"Action understanding has evolved into the era of fine granularity, as most human behaviors in real life have only minor differences. To detect these fine-grained actions accurately in a label-efficient way, we tackle the problem of weakly-supervised fine-grained temporal action detection in videos…
Supervisory control of a humanoid robot in a manipulation task requires coordination of remote perception with robot action, which becomes more demanding with multiple moving cameras available for task supervision. We explore the use of autonomous camera control and selection to reduce operator work…
Reservoir classification is an essential step for the exploration and production process in the oil and gas industry. An appropriate automatic reservoir classification will not only reduce the manual workloads of experts, but also help petroleum companies to make optimal decisions efficiently, which…
Robot teleoperation is a reliable way to perform a variety of tasks with complex robotic systems. However, the remote control of active telepresence cameras on the robot for improved telepresence adds an additional degree of complexity while teleoperating and can thus affect the operator’s performan…
Natural language processing (NLP) models often require a massive number of parameters for word embeddings, which limits their application on mobile devices. Researchers have employed many approaches, e.g. adaptive inputs, to reduce the parameters of word embeddings. However, existing methods rarely…
High-level control is generally preferred for the control of complex robot platforms and by users inexperienced with robot teleoperation. However, high-level teleoperation interfaces can be less effective if the robot autonomy is not reliable. To address this problem, it is important to understand h…
Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two item…
Telepresence tele-action robots enable human workers to reliably perform difficult tasks in remote, cluttered, and human environments. However, the effort to control coordinated manipulation and active perception motions may exhaust and intimidate novice workers. We hypothesize that such cognitive e…
Motion mapping is an intuitive method of teleoperation with a low learning curve. Our previous study investigates the physical fatigue caused by teleoperating a robot to perform general-purpose assistive tasks and this fatigue affects the operator's performance. The results from that study indicate…
Presentation attack is one of the main threats to face verification systems and attracts great attention of research community. Recent methods achieve great success in intra-database test. However, the problem is more complex in practical scenario as the type of attack could be unseen to system desi…
Handing over objects is the foundation of many human-robot interaction and collaboration tasks. In the scenario where a human is handing over an object to a robot, the human chooses where the object needs to be transferred. The robot needs to accurately predict this point of transfer to reach out pr…
The premise of training an accurate 3D human pose estimation network is the possession of huge amount of richly annotated training data. Nonetheless, manually obtaining rich and accurate annotations is, even not impossible, tedious and slow. In this paper, we propose to exploit monocular videos to c…
Robot teleoperation via motion mapping has been demonstrated to be an efficient and intuitive approach for controlling and teaching the whole-body motion coordination of humanoid robots. However, the physical fatigue in the usage of such robot teleoperation interfaces may prevent this approach to be…
High-fidelity computational human models provide a safe and cost-efficient method for studying driver experience in vehicle maneuvers and for validation of vehicle design. Compared to passive human models, active human models capable of reproducing the decision-making, as well as vehicle maneuver mo…
The addition of manipulation capabilities to telepresence robots holds the promise of enabling remote humans to perform tele-labor, hands-on training, and collaborative manipulation, but the use of a robot as a mediator to humanhuman physical interaction is not yet well understood. This paper studie…
During outbreaks of contagious diseases, healthcare workers are at high risk for infection due to routine interaction with patients, handling of contaminated materials, and challenges associated with safely removing protective gear. This poses an opportunity for the use of remote-controlled robots t…
This paper introduces two well-accepted path-following control methods, namely Vector Field Method (VF) and Line-Of-Sight Method (LOS) for Unmanned Surface Vehicles (USVs). We provide a comprehensive study of each algorithm, which includes investigating their mathematical origins, performing simulat…
Despite increasing popularity of compliant and underactuated hands, few tools are available for modeling them. Thus, we propose a simulation technique to predict the success of a compliant gripper grasping irregular objects, which could be used in mechanism design as well as grasp planning. The simu