124 accepted papers
Magnetic compression anastomosis (MCA) offers a promising solution for minimally invasive anastomosis surgery. However, current MCA schemes lack safe, real-time localization, and guidance for compression magnets, hindering surgeons’ ability to control the compression magnets effectively in complex, …
The grasping capabilities of robotic arms have been extensively studied by researchers in terms of accuracy, flexibility, and versatility, enabling robots to perform various tasks in domestic, industrial, and medical scenarios. However, grasping flat objects has remained a significant challenge and …
Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional settings. To mitigate this limitation, many divide-and-conquer frameworks have been proposed, but most a…
Anomaly segmentation seeks to detect and localize unknown or out-of-distribution (OoD) objects that fall outside predefined semantic classes--a capability essential for safe autonomous driving. However, the scarcity and limited diversity of anomaly data severely constrain model generalization in ope
Recent advances have shown that sequential fine-tuning (SeqFT) of pre-trained vision transformers (ViTs), followed by classifier refinement using approximate distributions of class features, can be an effective strategy for class-incremental learning (CIL). However, this approach is susceptible to d
Large Language Models (LLMs) have revolutionized intelligent interactions, enabling mobile applications such as personal assistants on edge devices for local execution. Speculative decoding (SD) has emerged as a promising paradigm to accelerate LLM inference without compromising generation quality,
Large language models still struggle to reliably answer questions grounded in real-world files like spreadsheets and slides, where evidence is scattered across irregular layouts and heterogeneous formats. We address this by formalizing File Reasoning, a setting where agents must interact directly wi…
Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based locomotion, their potential remains largely untapped for vision-centric tasks due to the prohibitive computational overhead …
Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of this technique hinges on the trade-off between the time spent on drafting candidates and verifying them. However, curre…
Causal effect estimation is a fundamental task in many scientific fields. Selecting appropriate covariates for adjustment is crucial for obtaining unbiased causal effects. However, most existing methods either rely on learning the global causal structure, assume the absence of latent variables, or i…
Deploying visual reinforcement learning (RL) policies in real-world manipulation is often hindered by camera viewpoint changes. A policy trained from a fixed front-facing camera may fail when the camera is shifted-an unavoidable situation in real-world settings where sensor placement is hard to mana
The ability to estimate the quality of scientific papers is central to how both humans and AI systems will advance scientific knowledge in the future. However, existing LLM-based estimation methods suffer from high inference cost, whereas the faster direct score regression approach is limited by sca…
Cross-view geo-localization is a critical task for UAV navigation, event detection, and aerial surveying, which establish correspondence between drone-captured and satellite imagery. Most existing approaches embed cross-view data into a joint feature space to maximize similarity between paired image
In real-world applications, video action recognition models must continuously learn new action categories while retaining previously acquired knowledge. However, most existing approaches rely on storing historical data for replay, which introduces storage burdens and raises data privacy concerns. To
The Transformer architecture, despite its widespread success, struggles with long-context scenarios due to quadratic computation and linear memory growth. While various linear attention variants mitigate these efficiency constraints by compressing context into fixed-size states, they often degrade p…
To continuously enhance model adaptability in surgical video scene parsing, recent studies incrementally update it to progressively learn to segment an increasing number of surgical instruments over time. However, prior works constantly overlooked the potential of positive forward knowledge transfer
Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training is restricted to aligning images with predefined known c…
Learning from preference feedback is essential for aligning large language models (LLMs) with human values and improving the quality of generated responses. However, existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale…
This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameteriz…
When dealing with multi-view data, the heterogeneity of data attributes across different views often leads to label ambiguity. To effectively address this challenge, this paper designs a Multi-View Partial-Label Learning (MVPLL) framework, where each training instance is described by multiple view f…
Despite of significant achievements in improving instruction-following capabilities of large language models (LLMs), the ability to process multiple potentially entangled or conflict instructions remains a considerable challenge. Real-world scenarios often require the consistency across multiple ins
Existing works have extensively studied adversarial examples, which are minimal perturbations that can mislead the output of deep neural networks (DNNs) while remaining imperceptible to humans. However, in this work, we reveal the existence of a harmless perturbation space, in which perturbations dr…
The remote center of motion (RCM) constraint is a vital requirement in the design of robotic systems for transrectal ultrasound (TRUS) probe-scanning. This paper presents the design and development of a novel RCM-constrained manipulator specifically tailored for TRUS probe-scanning applications. The
Although large language models (LLMs) have demonstrated outperforming human experts in medical examinations, it remains challenging to adopt LLMs in real-world clinical decision-making that typically involves multi-hop medical reasoning. Common practices include prompting commercial LLMs and fine-tu
Personality is a fundamental construct in psychology, reflecting an individual’s behavior, thinking, and emotional patterns. While previous researches have made progress in personality detection, their designed methods generally overlook the important connection between psychological knowledge “emot…
The open-source release of large language models (LLMs) enables malicious users to create unauthorized derivative models at low cost, posing significant threats to intellectual property (IP) and market stability. Existing IP protection methods either require access to model parameters or are vulnera…
Improving training efficiency continues to be one of the primary challenges in large-scale Reinforcement Learning (RL). In this paper, we investigate how context length and the complexity of training data influence the RL scaling training process of R1-distilled reasoning models, e.g., DeepSeek-R1-D
With the widespread adoption of Mixture-of-Experts (MoE) models, there is a growing demand for efficient inference on memory-constrained devices. While offloading expert parameters to CPU memory and loading activated experts on demand has emerged as a potential solution, the large size of activated…
Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reduc
Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. Currently, some studies are utilizing logical rules within evidence sentences to enhance the performance of DocRE. However, in cases where the data does not provide specific evidenc…
Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge for multi-hop re
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability. We introduce Hephaestus-Forge, the first large-scale pre-traini…
The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a…
We tackle the problem of identifying whether a variable is the cause of a specified target using observational data. State-of-the-art causal learning algorithms that handle latent variables typically rely on identifying the global causal structure, often represented as a partial ancestral graph (PAG…
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of laten…
Extensive research has been conducted to explore the capability of Large Language Models (LLMs) for table reasoning and has significantly improved the performance on existing benchmarks. However, tables and user questions in real-world applications are more complex and diverse, presenting an unignor…
Magnetically actuated capsule endoscopic robots (MACERs) are becoming increasingly popular because they can reach deep diseased regions inside the body that are difficult or inaccessible to traditional endoscopes without the restriction of mechanical transmission medium. However, MACERs are highly n
Despite advantages from neurosurgical systems, achieving intuitive and safe collaboration with robot during the coarse positioning of instrument insertion end effector (IIEE) remains a critical issue. In this paper, we propose a novel non-contact hand-guided method for such advancement based on magn
Performing target tracking and surveillance in dynamic obstacle environments requires maintaining continuous visual focus on the target while ensuring collision avoidance. This paper presents a safety-critical tracking control method that ensures dynamic obstacles remain outside the camera’s line of
In modern healthcare, the demand for autonomous robotic assistants has grown significantly, particularly in the operating room, where surgical tasks require precision and reliability. Robotic scrub nurses have emerged as a promising solution to improve efficiency and reduce human error during surger
Endoscopic submucosal dissection (ESD) is a procedure targeted for early gastrointestinal cancer. Traction plays a crucial role in enhancing the efficiency of cutting lesions, thereby reducing procedural complexity and duration. From the perspective of traction devices, current non-magnetic ones hol
Diffusion models have shown significant potential for image style transfer tasks. However, achieving effective stylization while preserving content in a training-free setting remains a challenging issue due to the tightly coupled representation space and inherent randomness of the models. In this pa
Ultrasound scanning is a critical imaging technique for real-time, non-invasive diagnostics. However, variations in patient anatomy and complex human-in-the-loop interactions pose significant challenges for autonomous robotic scanning. Existing ultrasound scanning robots are commonly limited to rela
The rapid advancement of conversational search systems revolutionizes how information is accessed by enabling the multi-turn interaction between the user and the system. Existing conversational search systems are usually built with two different models. This separation restricts the system from leve…
Recent advancements in unsupervised monocular depth estimation typically rely on an assumption that image photometry remains consistent across consecutive frames. However, this assumption often fails in endoscopic scenes due to: 1) local photometric inconsistency caused by specular reflections creat…
This letter presents a pneumatic artificial muscle-based elbow exosuit designed for driving and assisting the elbow joint. By combining the bionic attachment method inspired by the brachioradialis muscle and the pennation architecture of the artificial muscle, the elbow exosuit achieves high output
In response to torso-oriented magnetic localization tasks that require the system to have interactivity and flexibility with guaranteed accuracy, a novel bio-inspired magnetic sensor array configuration is proposed in this paper. Precisely, the ideas of the natural characteristics of octopus flexibl…
Understanding the deep semantics of images is essential in the era dominated by social media. However, current research works primarily on the superficial description of images, revealing a notable deficiency in the systematic investigation of the inherent deep semantics. In this work, we introduce…
"Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks. However, current learnable prompt tokens are primarily used for the single phase of adapting to tasks (i.e., adapting prompt), easily leading…
In Multi-View Multi-Label Learning, each instance is described by several heterogeneous features and associated with multiple valid labels simultaneously. Existing methods mainly focus on leveraging feature-level view fusion to capture a common representation for multi-label classifier induction. In…
Generative Adversarial Networks (GANs) have become widely used in model training, as they can improve performance and/or protect sensitive information by generating data. However, this also raises potential risks, as malicious GANs may compromise or sabotage models by poisoning their training data.…
The ability of large language models (LLMs) to execute complex instructions is essential for their real-world applications. However, several recent studies indicate that LLMs struggle with challenging instructions. In this paper, we propose Evolutionary Contrastive Distillation (ECD), a novel method…
Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, t…
Enhancing Language Models’ (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks. However, previous approaches that distill intentions from LMs often fail to generate meaningful and human-centric intentions appli…
With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcement learning with human feedback (RLHF) and Constitutional AI (CAI) have been proposed for LLM alignment. However, these…
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text en…
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with answer options for evaluation. However, many clinical decisions involve answering open-ended questions without pre-set o…
Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While current local structure learning methods have proven effective and efficient when the focus lies solely on the local relationships of a target variable, they…
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a considerable portion of self-updatable parameters, enabling the model to integrate new knowledge effectively and efficien…
Improving user experience and providing personalized search results in E-commerce platforms heavily rely on understanding purchase intention. However, existing methods for acquiring large-scale intentions bank on distilling large language models with human annotation for verification. Such an approa…
Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts neglecting the potential of prompts as effective distillers for learning f…
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Mode…
Hand extension is crucial for stroke survivors with spasticity, where their fingers become rigid and their thumb remains curled within the palm. Due to the underactuated nature of the hand, the dominance of flexor muscles over extensors, and the limited surface area available, developing an extensio…
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session d…
In minimally invasive surgery (MIS), the field of view (FOV) plays a vital role. To enhance the stability of FOV and lighten the burden on surgeons, robot-assisted laparoscope systems have been developed and introduced into surgery. However, most of the existing automatic surgical tool tracking sche
Characteristics of wireless manipulation based on permanent magnet coupling, including anchoring distance, panning torque, and translational force, are assessed in this paper. The study focuses on a typical scenario where a slave robot embedded with a small permanent magnet can be remotely controlle…
Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In general, the temperature controls the discrepancy between two distributions and can faithfully determine the diffi…
Recently, the newly emerged multimodal models, which leverage both visual and linguistic modalities to train powerful encoders, have gained increasing attention. However, learning from a large-scale unlabeled dataset also exposes the model to the risk of potential poisoning attacks, whereby the adve…
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs to supplement session data can lead to noisy correlations and user intent vanish…
Understanding users’ intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework, to reveal the structure of humans’ minds about purchasing items. As commonsense knowledge is usually ineffable and not exp…
Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e.g. graph neural networks (GNNs), to model their local structures and integrated into langu…
Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on task-agnostic distillation. It produces a compact pre-trained model that can be easily fine-tuned on various tasks with sma…
Current abstractive summarization models often generate inconsistent content, i.e. texts that are not directly inferable from the source document, are not consistent with respect to world knowledge, or are self-contradictory. These inconsistencies motivate a new consistency taxonomy that we define a…
Attribute Value Extraction (AVE) aims to retrieve the values of attributes from the product profiles. The state-of-the-art methods tackle the AVE task through a question-answering (QA) paradigm, where the value is predicted from the context (i.e. product profile) given a query (i.e. attributes). Des…
This paper is concerned with the issue of the kinematic model-based bending control for the magnetically actuated robotic endoscope and its application for automatic retroflexion. By the utilization of the Cosserat rod theory and the transformation in the magnetic tip of the endoscope, the comprehen…
In this letter, a multi-chamber soft bending pneumatic muscle (SBPM) is designed, which can achieve any-directional angle bending in space. The dynamic model of the multi-chamber SBPM is established by using Euler-Lagrange equation. To realize desired angle tracking control performances of the multi
Due to the high efficiency in extracting context information, voxel-based method is widely used in 3D object detection from point cloud. However, the quantization loss of geometric information is inevitable in the process of voxelization for raw point cloud, which may have a certain impact on final
Generating an informative and attractive title for the product is a crucial task for e-commerce. Most existing works follow the standard multimodal natural language generation approaches, e.g., image captioning, and employ the large scale of human-labelled datasets to train desirable models. However…
Prompt-based learning is vulnerable to backdoor attacks. Existing backdoor attacks against prompt-based models consider injecting backdoors into the entire embedding layers or word embedding vectors. Such attacks can be easily affected by retraining on downstream tasks and with different prompting s…
The soaring number of personal mobile devices and public cameras poses a threat to fundamental human rights and ethical principles. For example, the stolen of private information such as face image by malicious third parties will lead to catastrophic consequences. By manipulating appearance of face…
Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existin…
Kinematic calibration is crucial to improve the positioning accuracy of serial robots. This paper proposes a novel algorithm for robotic kinematic calibration based on an augmented product of exponentials (POE)-based kinematic model using Gaussian mixture models (GMMs) with only position data. In th…
Large language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) prompting. While CoT can yield dramatically improved performance, such gains are only observed for sufficiently large LMs. Even…
In image-guided robotic surgery, there exist different endoscopes coupled either with specialized surgical robots (SSRs) or general industrial robots (GIRs). In general, SSRs mechanically respect the remote-center-of-motion (RCM) constraints with directly and explicitly controllable degrees-of-freed
In minimally invasive surgery, endoscopes serve as the eyes of surgeon. To avoid fatigue in manual endoscope steering, robotic endoscope holders have been developed. Unfortunately, existing robotic endoscope holders are not widely adopted due to the poor surgeon-robot cooperation. In this work, we d
Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge in resource-constrained scenarios owing to their large memory requirements and high latency. To alleviate this issue, we…
Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signa…
This paper presents a long-range magnetic actuated and guided endoscope for uniport video-assisted thoracic surgery (VATS). In VATS, the incision is quite narrow and part of the chest wall may be very thick. So, the magnetic endoscope system is required to produce sufficient attractive force at a co…
The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF)
Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scen…
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized visi…
In this paper, we investigate a realistic but underexplored problem, called few-shot temporal knowledge graph reasoning, that aims to predict future facts for newly emerging entities based on extremely limited observations in evolving graphs. It offers practical value in applications that need to de…
Visual Semantic Embedding (VSE) is a dominant method for cross-modal vision-language retrieval. Its purpose is to learn an embedding space so that visual data can be embedded in a position close to the corresponding text description. However, there are large intra-class variations in the vision-lang…
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limit…
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven’t been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generatio…
Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified, and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim…
In this paper, we introduce a new open source toolkit for automatic speaker verification (ASV), named ASV-Subtools. Adopting PyTorch as main deep learning engine and Kaldi toolkit for data processing, ASV-Subtools allows users to develop modern speaker recognizers flexibly and efficiently. The toolk…
In this paper, we proposed an autonomous robotic flexible endoscope system for the laparoscopic bariatric surgery (LBS). This system comprises a UR5 robot and a flexible endoscope equipped with a novel continuum joint, named reinforced double helix continuum mechanism. Compared with the simple helix…
This letter presents the first case of implementing deep-learning based instrument tracking on a magnetic anchored surgical endoscope. The compact magnetic actuated endoscope has a unique structure that allows operations near the anchor surface, ideal for video assisted thoracoscopic surgery (VATS).
Driven by the SARS-CoV-2 pandemic, demand for oropharyngeal swab sampling (OP-swabs) is surging. However, medical staff can easily become infected by the virus during the sampling process. In an effort to combat this, we developed a novel, intrinsically safe rigid- flexible coupling (RFC) manipulato…
Crawling through various terrains has been a long research interest. In recent years, quite a number of soft crawling robots have been developed. However, locomoting in an elastic, humid, and slippery environment remains a challenge. In nature, gastropods, such as snails, live in humid environment a…
Sequence labeling aims to predict a fine-grained sequence of labels for the text. However, such formulation hinders the effectiveness of supervised methods due to the lack of token-level annotated data. This is exacerbated when we meet a diverse range of languages. In this work, we explore multiling…
Existing state-of-the-art human pose estimation methods require heavy computational resources for accurate predictions. One promising technique to obtain an accurate yet lightweight pose estimator is knowledge distillation, which distills the pose knowledge from a powerful teacher model to a less-pa…
Colorectal cancer incidence has been steadily rising worldwide. Magnetic colonoscopes provide new approaches to conduct colon inspection and treatment. This paper presents a novel electromagnetically actuated soft-tethered colonoscope to achieve precise and stable orientation control. An inflated ba…
This letter presents a novel flexible robotic endoscope with a constrained tendon-driven continuum mechanism (CTCM) which is targeted for bariatric surgery. The robotic endoscope is composed of a UR5 robot and a CTCM-based flexible endoscope. By introducing a constraint tube, both the angulation and
Soft robots are increasingly being employed in different fields and various designs are created to satisfy relevant requirements. The wide ranges of design bring challenges to soft robotic control in that a unified control framework is difficult to derive. Traditional model-driven approaches for sof
In stereotactic brain biopsy, operating the needle accurately and taking the biopsy specimen safely are two major challenges for ensuring the success of the surgical procedure. Considering this fact, surgical robots offering high accuracy and precision have been developed for neurosurgery including
This letter presents an experimental study on how different body flapping patterns affect the performances of fish cruising. First, a biomimetic robot fish is designed and built as the experimental platform, which mimics the skeleton structure and the muscle arrangement of real fish. Moreover, an im
Training of deep Convolution Neural Networks (CNNs) requires a tremendous amount of computation and memory and thus, GPUs are widely used to meet the computation demands of these complex training tasks. However, lacking the flexibility to exploit architectural optimizations, GPUs have poor energy ef…
Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic r…
A flexible endoscope introduces more dexterity to the image capturing in endoscopic surgery. However, manual control or automatic control based on instrument tracking does not handle the misorientation between the endoscopic video and the surgeon. We propose an automatic flexible endoscope control m…
Endoscopes play an important role in minimally invasive surgery (MIS). Due to the advantages of less occupied motion space and enhanced safety, flexible endoscopes are drawing more and more attention. However, the structure of the flexible section makes it difficult for surgeons to manually rotate a
Soft robots attract research interests worldwide. However, its control remains challenging due to the difficulty in sensing and accurate modeling. In this paper, we propose a novel iterative learning model predictive control (ILMPC) method for soft bending actuators. The uniqueness of our approach i…
Automation in robotic surgery has become an increasingly attractive topic. Although full automation remains fictional, task autonomy and conditional autonomy are highly achievable. Apart from the performance of task fulfillment, one major concern in robotic surgery is safety. In this paper, we prese
Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models. Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates. These boun…
Automation in surgery is becoming more and more active in recent years. From the editorial of Science Robotics in 2017, there are six levels of autonomy and the existing tele-operation lies in level 0 (no autonomy). Although full autonomy would be fictional in the current moment, task/shared autonom
This letter presents the first case of implementing visual servo control on a magnetic anchored endoscope. The novel magnetic actuated endoscope is designed for uniportal video-assisted thoracic surgery (VATS).Itis compact and has a workspace close to the chest wall, which is suitable for operation
Navigation through tubular environment is fundamental in tasks such as pipeline inspection, gastrointestinal tract inspection, etc. Conventional pipeline inspection robots are mostly made by rigid materials and could not well adapt to the large size variation of the environment. Soft robots provide…
This paper presents a novel magnetic anchored and steered camera robot intended for minimally invasive surgery (MIS), particularly for single port access (SPA) surgery. The design aims to achieve both compactness and a planar pan/tilt workspace (instead of hemispheric) to lower robot footprint in ve…
Minimally invasive surgery (MIS) has seen substantial development in recent decades. In MIS, particularly in laparoendoscopic single site (LESS) surgery, the surgical site is always crowded. One potential solution is the magnetic anchored and guidance system (MAGS), which liberates instruments from…
Continuum robots have been attracting extensive attentions in the past few years due to their potential applications in minimally invasive surgery (MIS). Force sensing is critical in surgical applications which require palpation or deal with delicate structures. This letter addresses on the force se
In this paper, a concentric wire-driven mechanism (CWM) is presented. The CWM comprises of two nested wire-driven mechanisms (WDM). Each WDM contains a flexible backbone and a set of wires. The backbone bending is controlled by pulling the wires. The stiffness of the outer WDM is controllable and do…
In this paper, a novel constrained tendon-driven serpentine manipulator (CTSM) suited for minimally invasive surgery is presented. It comprises of a flexible backbone, a set of controlling tendons and a constraint. In the CTSM not only the curvature of the bending section can be controlled but also…
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…