61 accepted papers
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through gradient propagation among primitives, optimization widely accepted in deep neural networks (DNNs) is actually adopted in 3DGS, such as synchronous we…
The Multi-Agent Path Finding (MAPF) problem seeks to find conflict-free paths for multiple agents. However, most existing MAPF methods simplify agents to points or uniform circles, a model that fails when agents have diverse geometries or carry oversized loads. This oversimplification can lead to un…
Vision-Language-Action (VLA) models have shown a strong capability in enabling robots to execute general instructions, yet they struggle with contact-rich manipulation tasks, where success requires precise alignment, stable contact maintenance,and effective handling of deformable objects. A fundamen…
In informed search-based path planning, heuristic functions that incorporate problem knowledge are essential for guiding the search and improving efficiency. The accuracy and computational cost of these heuristics are therefore critical to performance. However, accuracy and computational efficiency …
Biological synergies have emerged as a widely adopted paradigm for dexterous hand design, enabling human-like manipulation with a small number of actuators. Nonetheless, excessive coupling tends to diminish the dexterity of hands. This paper tackles the trade-off between actuation complexity and dex…
Three-dimensional (3D) measurement is essential for quality control in manufacturing, especially for components with complex geometries. Conventional viewpoint planning methods based on fixed spherical coordinates often fail to capture intricate surfaces, leading to suboptimal reconstructions. To ad
Whole Slide Image (WSI) analysis faces challenges due to gigapixel resolutions and slide-level weak supervision. Multiple Instance Learning (MIL) serves as a pivotal method for this task. However, existing MIL frameworks often fail to exploit the inherent redundancy of tissue patterns or the semanti
3D Gaussian Splatting (3DGS) enables real-time, high-fidelity view synthesis via explicit scene representations and has recently been extended to dynamic scene modeling. In spite of excellent quality and interpretability, we find explicit Dynamic GS often exhibits generalization degradation in large…
3D shape generation has become increasingly important for graphics and vision applications. Current part-aware 3D generation usually overlooks hierarchical part relations or inefficiently encodes multi-level semantics in Euclidean space. Thus we propose a novel framework for hierarchical and efficie
Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-super
Monocular 3D Visual Grounding (Mono3DVG) is an emerging task that locates 3D objects in RGB images using text descriptions with geometric cues. However, existing methods face two key limitations. Firstly, they often over-rely on high-certainty keywords that explicitly identify the target object whil
This letter presents a saturation-triggered adaptive fault-tolerant control framework for underwater vehicles to handle concurrent thruster failures and environmental disturbances. Unlike existing methods that suffer from performance degradation when disturbances exceed predefined bounds, this paper
Coded Aperture Snapshot Spectral Imaging (CASSI) has emerged as a prominent technique for efficient hyperspectral imaging. However, the tight coupling between physical encoding and computational decoding makes CASSI highly sensitive to slight hardware misalignments, which can significantly degrade r
The simultaneous arrival of multiple mobile robots at their respective target points is crucial for cooperative tasks such as encirclement, interception, and disaster relief. Although the problem of simultaneous arrival is inherently complex, it becomes even more challenging in multi-robot systems w…
Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring functional grasps needed for tasks like opening bottle caps or holding cup handles. Most rely on bulky, costly, and hard-to-control high-DOF Shadow Hands. Inspired by the human hand's underac
Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring functional grasps needed for tasks like opening bottle caps or holding cup handles. Most rely on bulky, costly, and hard-to-control high-DOF Shadow Hands. Inspired by the human hand’s underac…
Underwater vehicles face control challenges including hydrodynamic parameter uncertainties and environmental disturbances. While Koopman-based model predictive control (Koopman-MPC) offers computational efficiency through linear lifted representations, existing methods rely on offline-collected data
Aerial-terrestrial amphibious robots excel in search and rescue tasks in unstructured terrains but face challenges in autonomous navigation indoors. Traditional full-mapping methods can degrade global path planning performance, especially when semi-static obstacles shift, leading to suboptimal paths
This paper proposes a Parallel Fuzzy Nonlinear Active Disturbance Rejection Control (FNLADRC) strategy to improve the precision and robustness of robotic manipulators in machining large, complex components. By decoupling the multi-degree-of-freedom dynamics and integrating Nonlinear ADRC with fuzzy
Parameter-Efficient Fine-Tuning (PEFT) has emerged to mitigate the computational demands of large-scale models. Within computer vision, adapter-based PEFT methods are often favored over prompt-based approaches like Visual Prompt Tuning (VPT) due to the latter's performance and efficiency limitations…
The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances. By formulating lateral deviations as energy-equivalent penalties in the cost fu
Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are…
Formulating a multi-robot obstacle avoidance policy is essential for enabling safe and efficient navigation in multi-robot environments, forming a critical component of the effective operation of multi-robot systems. Recently, reinforcement learning has been applied to improve the performance of dec
This paper investigates the control problem of underwater vehicles subject to time-varying external disturbances and actuator faults. A novel passive fault-tolerant control (PFTC) scheme is developed to address the coupled disturbance-fault dynamics inherent in underwater vehicle systems. The propos
Multimodal 3D object detectors leverage the strengths of both geometry-aware LiDAR point clouds and semantically rich RGB images to enhance detection performance. However, the inherent heterogeneity between these modalities, including unbalanced convergence and modal misalignment, poses significant
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
Tiny object detection remains challenging in spite of the success of generic detectors. The dramatic performance degradation of generic detectors on tiny objects is mainly due to the the weak representations of extremely limited pixels. To address this issue, we propose a plug-and-play architecture…
Unsupervised domain adaptation (UDA) is an effective scheme to improve the performance of an object detector in foggy scenes by adapting labeled normal images (source domain) to unlabeled foggy images (target domain). Existing methods leverage the Teacher-Student mutual learning framework, <italic x
Semantic Scene Completion (SSC) aims to reconstruct the entire 3D scene in terms of both occupancy and semantics, serving as a fundamental task for autonomous driving and robotic systems. Camera-based methods have seen significant advancements due to their low cost and rich visual cues. However, pre
3D content creation has achieved significant progress in terms of both quality and speed. Although current Gaussian Splatting-based methods can produce 3D objects within seconds, they are still limited by complex preprocessing or low controllability. In this paper, we introduce a novel framework des…
The multi-agent pickup and delivery problem is central to coordinating multiple agents in real-world applications such as warehouse automation, urban logistics, and robotic delivery networks, where efficient task assignment and pathfinding are vital for maximizing production efficiency. However, exi
Place recognition is a fundamental technology of high relevance for autonomous robot navigation. Existing methods encounter significant challenges arising from scene variations (e.g., illumination changes, dynamic objects), view-point shifts, and difficulties in data fusion and alignment. These fact
Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulati
Homography estimation is fundamental to various vision tasks. Iteration-based methods have recently achieved significant success in this field. However, errors introduced during iterations can lead to increased image deformation. Existing methods often focus on capturing local correspondences in the…
In contact-rich tasks such as polishing and drilling, inevitable physical interactions often lead to task deviations due to interference, typically resulting in excessive contact forces and eventual task failure. To tackle these challenges, we propose an innovative human-guided visual-impedance cont
Motion planning for continuous contact-based aerial manipulators on complex unstructured surfaces remains a substantial challenge due to the sophisticated topology of unstructured surfaces. While direct planning in the high-dimensional configuration space manifolds faces efficiency limitations, simp
Text to point cloud cross-modal localization is a crucial vision-language task for future human-robot collaboration. Existing coarse-to-fine frameworks assume that each query text precisely corresponds to the center area of a submap, limiting their applicability in real-world scenarios. This work re…
Person re-identification (re-id) models are vital in security surveillance systems, requiring transferable adversarial attacks to explore the vulnerabilities of them. Recently, vision-language models (VLM) based attacks have shown superior transferability by attacking generalized image and textual f…
Since its inception, impedance control has emerged as a fundamental framework for robotic interaction control. Recent advancements in geometric impedance control have demonstrated certain advantages over traditional Cartesian impedance control. However, existing geometric impedance control approache
Existing NAS methods for semantic segmentation typically apply uniform optimization to all candidate networks (paths) within a one-shot supernet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is…
Visual cross view geo-localization is generally approached within a joint retrieval-and-calibration framework. However, existing methods overlook semantic ambiguities arising from query and reference images characterized by low overlap, dynamic foregrounds, viewpoint changes, and perceptual aliasing…
Air-ground robots have received more and more attention and applications due to their air-to-ground motion performance and excellent energy efficiency. However, airground robots have many gaps including complex structure mechanisms, low terrain adaptability and low-precision controllers to significa
Mainstream visual-inertial SLAM systems use point features for motion estimation and localization. However, point features do not perform well in scenes such as weak texture and motion blur. Therefore, the introduction of line features has received a lot of attention. In this letter, we propose a po
Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the policy network to effectively capture spatial-temporal reciprocal collision avoidan…
For aerial swarms, formation flight has been applied in various scenes. However, most existing works do not consider balancing the conflicting requirements among keeping formation, keeping the smoothness of trajectories, and obstacle avoidance within the limited time. To address this issue, we propo…
Generalizing visual reinforcement learning is fundamental to robot visual navigation, involving the acquisition of a policy from interactions with source environments to facilitate adaptation to analogous, yet unfamiliar target environments. Recent advancements capitalize on data augmentation techni…
"Generating realistic 3D scenes is challenging due to the complexity of room layouts and object geometries. We propose a sketch based knowledge enhanced diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes. SEK conditions the denoising process with a hand-drawn sk…
3D visual language multi-modal modeling plays an important role in actual human-computer interaction. However the inaccessibility of large-scale 3D-language pairs restricts their applicability in real-world scenarios. In this paper we aim to handle a real-time multi-task for 6-DoF pose tracking of u…
Diffusion probabilistic models have achieved remarkable success in text guided image generation. However, generating 3D shapes is still challenging due to the lack of sufficient data containing 3D models along with their descriptions. Moreover, text based descriptions of 3D shapes are inherently amb…
Highly accurate and efficient map in unknown and complex environments is essential for robotics navigation. Traditionally, mobile robot platforms are often computationally constrained when using multiple sensors to process large amounts of input data. In previous works, some of them have been deploy…
Robots that are supposed to interact with or manipulate objects in the world must be able to track the poses of objects in their sensor data. Thus, Detecting and tracking the 6-DoF poses of targeted objects is important for aerial manipulation and is still in the early stage due to the high dynamics…
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate. In this paper, we propose to learn light field saliency from pixel-level noisy labels obta…
This paper addresses the generation mechanism and avoidance method of negative stiffness during in-Hand manipulation with underactuated compliant hands. Firstly, a planar hand with two three-jointed fingers manipulating a rectangular is set, and a quasi-static underactuated operation model is establ…
Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, mo…
3D object grounding aims to locate the most relevant target object in a raw point cloud scene based on a free-form language description. Understanding complex and diverse descriptions, and lifting them directly to a point cloud is a new and challenging topic due to the irregular and sparse nature of…
Autonomous intersection management (AIM) refers to planning the trajectories for multiple connected and automated vehicles (CAVs) when they traverse an unsignalized intersection cooperatively. As an extension of the conventional AIM, lane-free AIM allows the CAVs to adjust their velocities and paths…
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a…
Hand synergy from neuroscience provides an effective tool for anthropomorphic hands to realize versatile grasping with simple planning and control. This paper aims to extend the synergy-inspired design from anthropomorphic hands to multi-fingered robot hands. The synergy-inspired hands are not neces…
Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI…
Flexible serpentine manipulators are widely used in surgical robots as it can be operated inside the patient's body cavity by backbone bending. However, during the bending the manipulator sweeps over a region, where sensitive organs may locate. This raises the safety concern. In this paper, a motion…