34 accepted papers
In teleoperated surgery, the motion scaling factor directly influences both the operator’s control precision of surgical instruments and operational comfort. Previous studies have revealed that the master manipulator state and operator’s gaze information can reflect the complexity of surgical operat
Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied systems faces challenges in ensuring reliable execution of subtask sequences and achieving one-shot success in long-term
Autonomous suturing is a critical challenge in robot-assisted surgery, where accurate segmentation and pose estimation of suturing threads are essential prerequisites. However, suturing threads are easily occluded by moving instruments and embedded in deformable tissues which make the task much more
Rehabilitation robotics has attracted increasing attention due to its ability to provide continuous, precise, and adaptive treatment programs for stroke patients during their recovery. Accurately assessing lower-limb motor function is crucial in effectively implementing robot-assisted rehabilitation
Invasive flexible neural electrodes are becoming increasingly prevalent in monitoring and modulating brain neural activity, necessitating the precise and minimally invasive implantation of these electrodes to a depth of a few millimeters beneath the cerebral surface. Although Neuralink has pioneered
Photoacoustic Microscopy (PAM) is a relatively new imaging modality in biomedicine. However, point-by-point raster scanning in PAM suffers from low imaging speed. Sparse sampling has been studied in recent years and with the development of deep learning algorithms, extensive efforts have been devote…
The increasing awareness of public health issues has highlighted the need for effective disinfection of crowded indoor public areas, leading to the development of automated disinfection robots. However, most of the existing robots spray disinfectant in all areas, and they are still immature to navig…
This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation in faithful PatchMatch multi-view stereo (MVS). We observe that the heuristic depth hypothesis sampling modes employed by PatchMatch MVS solvers are insensitive…
For the development of robotic systems for micromanipulation, it is challenging to design appropriate control strategies due to either the lack of sufficient information for feedback or the difficulty in extracting subtle yet critical visual features. With the same system under the teleoperated mode…
Eye gaze can convey rich information of human intentions, which enables the social robots to comprehend the cognition and behavior of human targets. However, the existing 3D gaze estimation methods generally have high requirements either on the dedicated hardware or the quantity and quality of train…
Egocentric vision has gained increasing popularity in social robotics, demonstrating great potentials for personal assistance and human-centric behavior analysis. Holistic per-ception of human body itself is a prerequisite for downstream applications, including action recognition and anticipation. E…
Holographic acoustic field has shown great potential for non-contact robotic manipulations of millimeter or sub-millimeter size objects to effectively deliver acoustic power. The latest technology for generating dynamic holographic acoustic field is through phased transducer array, where relative ph
Egocentric vision is an emerging topic, which has demonstrated great potential in assistive healthcare scenarios, ranging from human-centric behavior analysis to personal social assistance. Within this field, due to the heterogeneity of visual perception from first-person views, egocentric pose esti…
Human-robot shared control, which integrates the advantages of both humans and robots, is an effective approach to facilitate efficient surgical operation. Learning from demonstration (LfD) techniques can be used to automate some of the surgical sub tasks for the construction of the shared control m…
MOBA (Multiplayer Online Battle Arena) games such as Dota2 are currently one of the most popular e-sports gaming genres. Following professional commentaries is a great way to understand and enjoy a MOBA game. However, massive game competitions lack commentaries because of the shortage of professiona…
Vision-based 3D human pose estimation and shape reconstruction play important roles in robot-assisted healthcare monitoring and personal assistance. However, 3D data captured from a single viewpoint always encounter occlusions and exhibit substantial heterogeneity across different views, resulting i…
Acoustic holography is a newly emerging and promising technique to dynamically generate arbitrary desired holographic acoustic field in 3D space for contactless robotic manipulation. The latest technology supporting complex dynamic holographic acoustic field reconstruction is through phased transduc…
"Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2) the test data is drawn from different conditional distributions P(X|Y). Existing approaches cannot handle the scenario…
3D dense captioning aims to describe individual objects by natural language in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though ag…
Research on generating or processing point clouds has become an increasingly popular domain in robotic research due to its extensive applications, such as robotic grasping, augmented reality and autonomous vehicle navigation. In this paper, we explore a new research area on point clouds - Learning t…
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emerg…
Simultaneous assembly of multiple objects is a key technology to form solid connections among objects to get compact structures in precision assembly and micro-assembly. Dramatically different from traditional assembly of two objects, the interaction among multiple objects is more complicated on ana…
The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focuses on optimizing the weights of pre-trained models, which ignores the structure mismatch between the model and the targe…
Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on smallscale datasets or synthetic data due to the difficulties of ground-truth an…
The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their impl…
Assistive robots play an important role in improving the quality of life of patients at home. Among all the monitoring tasks, gait disorders are prevalent in elderly and people with neurological conditions and this increases the risk of fall. Therefore, the development of mobile systems for gait mon
Accurate master-slave control is important for Robot-Assisted Microsurgery (RAMS). This paper presents a handheld master controller for the operation and training of RAMS. A 9-axis Inertial Measure Unit (IMU) and a micro camera are utilized to form the sensing system for the handheld controller. A n…
In this paper, we present a novel approach for surgical task segmentation. A segmentation policy learns the correlations between features and segmentation points from manually labeled data. The most correlated features and rules for segmenting them are identified and learned. These form a complete s…
In surgical workflow analysis and training in robot-assisted surgery, automatic task segmentation could significantly reduce the manual labeling time and enhance robot learning efficiency. This paper presents an unsupervised segmentation approach to automatically segment a given surgical task withou…
As increasing attention is paid on human action recognition from skeleton data, this paper focuses on such tasks by proposing a hierarchical model to discover the structure information of body-parts involved in human actions. Considering human actions as simultaneous motions of different body-parts…
This paper deals with sound source localization and number estimation in indoor environments using a circular microphone array. Multiple sound source localization is achieved by performing single source localization at each selected time-frequency (TF) point of received signals after short-time Four…
This paper proposes a flexible descriptor for representing 6-D rigid body motion trajectories, which not only shows strong invariances and descriptive ability but also achieves satisfactory results in both recognition accuracy and efficiency. 6-D rigid body motion trajectories are first transformed…