74 accepted papers
Multi-robot systems have demonstrated significant potential in accomplishing complex tasks, such as cooperative pursuit, search-and-rescue operations. The emergence of heterogeneous robots with diverse capabilities and characteristics shows superior adaptability compared with homogeneous teams. Howe
Comprehension of ancient texts plays an important role in archaeology and understanding of Chinese history and civilization. The rapid development of large language models needs benchmarks that can evaluate their comprehension of ancient characters. Existing Chinese benchmarks are mostly targeted at
Stereo depth estimation has drawn widespread attention from the robotics and vision community due to its broad applications such as 3D reconstruction. Recently, stereo matching foundation models have made significant progress by being trained on the large-scale datasets containing natural images. Ho…
3D Gaussian Splatting (3DGS) has been widely adopted for scene reconstruction, where training inherently constitutes a highly coupled and non-convex optimization problem. Recent works commonly incorporate geometric priors, such as LiDAR measurements, either for initialization or as training constrai…
We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption that fails in practice as objects deviate from their canonical shapes due to wear, impact, or deformation. To model this, we introduce the DeSOPE dataset
Context modeling is fundamental to LiDAR point cloud compression. Existing methods rely on computationally intensive 3D contexts, such as voxel and octree, which struggle to balance the compression efficiency and coding speed. In this work, we propose a neural LiDAR compressor based on 2D context mo…
Semantic Scene Completion (SSC) is essential for 3D perception in mobile robotics, as it enables holistic scene understanding by jointly estimating dense volumetric occupancy and per-voxel semantics. Although SSC has been widely studied in terrestrial domains such as autonomous driving, aerial setti
End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual understanding and strong reasoning capabilities obtained from the large-scale pretraining. However, we find that current VLMs struggle to understand fine-gra
Robots in dynamic, human-centric environments must follow language instructions while maintaining real-time reactive control. Vision-language-action (VLA) models offer a promising framework, but they assume temporally aligned reasoning and control, despite semantic inference being inherently delayed…
In computer-assisted orthopedic surgery (CAOS), accurately registering sparse and partial intraoperative point sets with a complete preoperative model remains highly challenging due to limited overlap, extreme sparsity, and point localisation noise. In this paper, we propose a novel end-to-end compl…
Soft grippers conform to the shape and surface properties of the objects to be grasped, effectively avoiding damage to soft and fragile items. Despite the variety of existing soft gripper designs, their structures lack sufficient flexibility for effectively grasping slender objects or operating in n
The precise identification of the robot curtain wall assembly state is crucial for improving construction efficiency. Traditional methods remain sensitive to noise in high-dimensional sensor data and require extensive datasets, resulting in limited generalization. A method for identifying the robot
Negotiation agents need to influence the attitudes or intentions of users to reach a consensus. Strategy planning and expressive optimization are crucial aspects of effective negotiations. However, previous studies have typically focused on only one of these aspects, neglecting the fact that their c…
Dynamic scene rendering opens new avenues in autonomous driving by enabling closed-loop simulations with photorealistic data, which is crucial for validating end-to-end algorithms. However, the complex and highly dynamic nature of traffic environments presents significant challenges in accurately re…
Goal-conditioned reinforcement learning (GCRL) is an effective method for multi-goal robotic manipulation tasks. Many studies based on hindsight experience replay (HER) and hindsight goal generation (HGG) have achieved the autonomous acquisition of robotic manipulation in reward-sparse environments
Multimodal sarcasm detection (MSD) leverages multimodal data, including both images and text, to detect whether the input content contains sarcastic information. Despite recent advances, existing MSD approaches often overlook the imbalance in sarcastic content between text and image modalities, wher…
3D point cloud registration is an essential problem in computer vision, robotics, surgical navigation and augmented reality. Accurate registration of partially overlapped intraoperative point clouds (e.g., femoral reconstruction) remains critical yet challenging in orthopedic navigation due to incom
Fisheye cameras, renowned for their panoramic field of view (FOV) of 360°, are crucial for surround-view perception in autonomous driving. However, research on object perception in fisheye images lags behind that of standard images. To address this gap, we propose a feature-aligned fisheye object de
Semantic Scene Completion (SSC) has emerged as a pivotal approach for jointly learning scene geometry and semantics, enabling downstream applications such as navigation in mobile robotics. The recent generalization to Panoptic Scene Completion (PSC) advances the SSC domain by integrating instance-le…
Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings where multiple units are present in the same round, interference can significantly affect the estimatio…
Off-Policy Evaluation (OPE) aims to estimate the value of a target policy using offline data collected from potentially different policies. In real-world applications, however, logged data often suffers from missingness. While OPE has been extensively studied in the literature, a theoretical underst…
In computer-assisted orthopedic surgery (CAOS), accurate point set registration is essential for enhancing surgical accuracy. However, the sparse and low-overlap nature of intraoperative point sets presents significant challenges for reliable registration. To deal with these challenges, we propose a
In this paper, we present a novel curve-to-surface registration method, termed Bi-directional Hybrid Mixture Model Registration based on Dual-constrained Tangent and Normal Vectors (BiHMM-DTN), where two different tangent vectors at the intraoperative point are simultaneously used with the normal ve
Tactile recognition enables humanoid robots to interact naturally and intelligently. Neuromorphic models, known for their robustness, efficiency, and low energy consumption, offer significant potential. Existing approaches primarily focus on normal force, neglecting other signals. Moreover, the impr
Robust and accurate 2D/3D registration, which aligns preoperative models with intraoperative images of the same anatomy, is crucial for successful interventional navigation. To mitigate the challenge of a limited field of view in single-image intraoperative scenarios, multi-view 2D/3D registration i
We introduce SCFlow2, a plug-and-play refinement framework for 6D object pose estimation. Most recent 6D object pose methods rely on refinement to get accurate results. However, most existing refinements either suffer from noises in establishing correspondences, or rely on retraining for novel objec…
We tackle the problem of localizing traffic cameras within a 3D reference map and propose a novel image-to-point cloud registration (I2P) method, TrafficLoc, in a coarse-to-fine matching fashion. To overcome the lack of large-scale real-world intersection datasets, we first introduce Carla Intersect…
In this paper, we propose a novel unsupervised intraoperative liver deformation correction method, called Learning Coherent point drift Network (LCNet), for image-guided liver surgery (IGLS). We first estimate the correspondences between the preoperative and intraoperative point sets in the optimal
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful distribution modeling is recognized as essential for improving estimation efficiency in statistics. However, this approach r…
In this paper, we introduce Bidirectional Non-Overlapping Filtering Network (Bi-NOFNet), which registers the partial intraoperative point set with full preoperative point set for computer-assisted interventions (CAI). Our contributions are three-folds. First, Bi-NOFNet adopts customised feature extr…
Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However th…
Human-like social bias of pre-trained language models (PLMs) on downstream tasks have attracted increasing attention. The potential flaws in the training data are the main factor that causes unfairness in PLMs. Existing data-centric debiasing strategies mainly leverage explicit bias words (defined a…
In this paper, we introduce a novel normal-assisted learning-based rigid registration approach, i.e., Deep Bi-directional Hybrid Mixture Registration (DeepBHMR). Our approach utilises helpful normal vectors explicitly in both correspondence and transformation stages and formulates the optimization o
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AES based on domain knowledge. However, this method becomes impractical for online experimentation services managing numer…
The proficiency of robots in cloth manipulation is crucial for their potential widespread deployment in household service contexts, with the task of unfolding cloth being particularly indispensable. Unlike rigid objects, cloth has a high-dimensional state space, which poses significant challenges fo
It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This str…
In this paper, we introduce a novel overlap-based bidirectional point set registration approach, i.e., Overlap-guided Bidirectional Hybrid Mixture Registration (OBHMR), which incorporates geometric information (i.e., normal vectors) in both the correspondence and transformation stages and formulates…
Posterior sampling in contextual bandits with a Gaussian prior can be implemented exactly or approximately using the Laplace approximation. The Gaussian prior is computationally efficient but it cannot describe complex distributions. In this work, we propose approximate posterior sampling algorithms…
Recently, there has been significant interest in replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs), such as Direct Preference Optimization (DPO) and its variants. These approaches commonly use a binary cross-entropy mechanism on…
Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domaininvariant features. However, these methods rely on unlabeled…
Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensors increases reliability and extends the sensor range. External sensors offer higher situational awareness for automated…
Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes, and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on point-exposure studies where each subject only receives one trea…
Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy. In many cases, there exist unmeasured variables that confound the action-reward or action-next-state relationships, rendering m…
Existing in-pipe robots have insufficient adaptability when dealing with accidents in unfamiliar pipe environments. Developing a pipe robot that can be designed and manufactured quickly is one solution. The tensegrity structure is a self-stressing spatial structure formed by the interaction of rigid…
Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy g…
Ontology serves as the foundation for the underlying representation of knowledge. In order to achieve the sharing of knowledge across languages, ontologies that are typically only represented in English must be translated into different languages. Building a domain-specific translation system is nec…
Deep Neural Networks (DNNs) are rather restrictive in long-tailed data, since they commonly exhibit an under-representation for minority classes. Various remedies have been proposed to tackle this problem from different perspectives, but they ignore the impact of the density of Backbone Features (BF…
Snap-fit recognition is an essential capability for industrial robots in manufacturing. The goal is to protect fragile parts by quickly detecting snap-fit signals in the assembly. In this letter, we propose a fast recognition method of snap-fit for industrial robots. A snap-fit dataset generation st
Deformable object manipulation has been considered a challenging task for robots for its complex dynamics and the infinite dimensional configuration space. Fabric unfolding manipulation takes on critical significance in the textile industry and household services. Accordingly, enabling robots to pos
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty. In this paper, we propose Multiplier Bootstrap-based Exploration (MBE), a novel exploration strategy that is appli…
Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare problems that greatly hampered research on developing effective treatment and understanding of the underlying neurobiological mechanism. Very few studies have been conducted to investigate heterogeneous ca…
The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all variables within a complex large-scale graph. However, in practice, only a small subset of variables in the graph are re…
Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range. In this paper, we propose a 6D object pose estimation method that can be trained with pure RGB images…
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose networ…
Most recent 6D object pose estimation methods rely on 2D optical flow networks to refine their results. However, these optical flow methods typically do not consider any 3D shape information of the targets during matching, making them suffer in 6D object pose estimation. In this work, we propose a s…
Online learning in large-scale structured bandits is known to be challenging due to the curse of dimensionality. In this paper, we propose a unified meta-learning framework for a wide class of structured bandit problems where the parameter space can be factorized to item-level, which covers many pop…
Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception…
Interactive argument pair identification is an emerging research task for argument mining, aiming to identify whether two arguments are interactively related. It is pointed out that the context of the argument is essential to improve identification performance. However, current context-based methods…
Most current in-pipe robots are usually designed for pipes of a specific size. In this letter, we propose a novel inchworm-like in-pipe robot based on the concept of tensegrity for moving in pipes with varying diameters. Firstly, a tensegrity-based robotic module capable of two kinds of shape change
This paper presents a novel concept to develop robots capable of crawling in tubular environments, inspired by the movement of earthworms and the biological musculoskeletal systems in nature. A tensegrity structures-based robotic module with shape changeability actuated by only one linear actuator i…
Federated Learning (FL) is an emerging privacy-aware machine learning technique that applies successfully to the collaborative learning of global models for Human Activity Recognition (HAR). As of now, the applications of FL for HAR assume that the data associated with diverse individuals follow the…
With the growing popularity of deep-learning models, model understanding becomes more important. Much effort has been devoted to demystify deep neural networks for better explainability. Some feature attribution methods have shown promising results in computer vision, especially the gradient-based m…
This study focuses on localization and mapping for UGVs when they are deployed in environments with non-level roads. In these scenarios, the vehicles need to travel through flat but not necessarily level grounds, i.e., ascent or descent, which may cause drifts of the robot pose and distortion of the…
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework ca…
High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several representative variants. For each variant, we analyze its stat…
In the era of causal revolution, identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas, such as epidemics, medicine, genetics, and economics. Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirec…
We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete tr…
Off-policy evaluation learns a target policy’s value with a historical dataset generated by a different behavior policy. In addition to a point estimate, many applications would benefit significantly from having a confidence interval (CI) that quantifies the uncertainty of the point estimate. In thi…
How to explore efficiently is a central problem in multi-armed bandits. In this paper, we introduce the metadata-based multi-task bandit problem, where the agent needs to solve a large number of related multi-armed bandit tasks and can leverage some task-specific features (i.e., metadata) to share k…
In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime which optimizes the short-term outcomes for the patients, we define the weighted lag K advantage. The optimal treatment regime is t…
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning. In this paper, we propose a novel Forward-Backward Learning procedure to test MA in sequential decision making. The proposed test does not assume any parametric form on the joint distribution of the observ…
In the current era of personalized recommendation, one major interest is to develop an optimal individualized decision rule that assigns individuals with the best treatment option according to their covariates. Estimation of optimal decision rules (ODR) has been extensively investigated recently, ho…
The interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a…
As a key component in many computer vision systems, optical flow estimation, especially with large displacements, remains an open problem. In this paper we present a simple but powerful matching method works in a coarse-to-fine scheme for optical flow estimation. Inspired by the nearest neighbor fie…