23 accepted papers
Deformable Linear Objects (DLOs), such as cables and ropes, pose significant challenges for robotic manipulation due to their high-dimensional state space, nonlinear deformation dynamics, and strong sensitivity to external forces. Cable routing tasks, in particular, are further complicated by geomet…
Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient. To address this, previous work has introduced pushing as an auxiliary action to create graspable space. However, these m…
Vision-Language-Action models (VLAs) are emerging as powerful tools for learning generalizable visuomotor control policies. However, current VLAs are mostly trained on large-scale image–text–action data and remain limited in two key ways: (i) they struggle with pixel-level scene understanding, and (…
Interactive 3D segmentation embodies an advanced human-in-the-loop paradigm, where a model iteratively refines the segmentation of interested objects within a 3D point cloud through user feedback. Existing methods have achieved notable advancements at the expense of substantial resource consumption.
Online monocular 3D reconstruction has attracted widespread attention as it promotes the application of robots in interactive scenarios. Most existing methods focus on 1) real-time reconstruction, 2) accurate voxel featuring learning, and 3) effective voxel sparsification algorithm. To this end, 1)
Learning robust and generalizable manipulation skills from few demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. Although recent imitation learning methods have achieved impressive results, they often require a large amount of
"Incremental semantic segmentation endeavors to segment newly encountered classes while maintaining knowledge of old classes. However, existing methods either 1) lack guidance from class-specific knowledge (i.e., old class prototypes), leading to a bias towards new classes, or 2) constrain class-sha…
Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly optimizing the pose from pixel loss between rende…
In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detect…
In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention. Generally, most existing methods for deformable object manipulation suffer two issues, 1) Massive demonstration: repeating thousan…
Federated learning-based semantic segmentation (FSS) has drawn widespread attention via decentralized training on local clients. However, most FSS models assume categories are fxed in advance, thus heavily undergoing forgetting on old categories in practical applications where local clients receive…
Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all old categories have the same forgetting pace, and neglect negative infl…
Gesture recognition is a popular human-computer interaction technology, which has been widely applied in many fields (e.g., autonomous driving, medical care, VR and AR). However, 1) most existing gesture recognition methods focus on the fixed recognition scenarios with several gestures, which could…
Recent progress in introducing rotation invariance (RI) to 3D deep learning methods is mainly made by designing RI features to replace 3D coordinates as input. The key to this strategy lies in how to restore the global information that is lost by the input RI features. Most state-of-the-arts achieve…
Visual-tactile fused sensing for object clustering has achieved significant progresses recently, since the involvement of tactile modality can effectively improve clustering performance. However, the missing data (i.e., partial data) issues always happen due to occlusion and noises during the data c…
Shape correspondence from 3D deformation learning has attracted appealing academy interests recently. Nevertheless, current deep learning based methods require the supervision of dense annotations to learn per-point translations, which severely over-parameterize the deformation process. Moreover, th…
Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation. However, 1) existing methods neglect that not all semantic representations across domains are transferable, which cripples domain-wise transfer with un…
Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion. Generic detection algorithms can not work well when we use them directly in the underwater scene. To address these problems, we propose an…
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been p…
Most state-of-the-art underwater vision systems are calibrated manually in shallow water and used in open seas without changing. However, the refractivity of the water is adaptively changed depending on the salinity, temperature, depth or other underwater environmental indexes, which inevitably gene…
Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse les…
In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression t…
This paper proposed a phase-closed-loop nanomachining control method to realize the directly control of machining depth based on ultrasonic vibration-assisted AFM. By using applied force to control the machining depth, conventional AFM machining approaches unable to machining a nanostructure with sp…