56 accepted papers
To enhance the motion adaptability of wheel-legged robot in complex environments, a novel data-driven value iteration (VI) control algorithm based on adaptive dynamic programming (ADP) is proposed. A state-space system model of the wheel-legged robot is established, from which the algebraic Riccati
What if the next generation of human-computer interaction is not a screen... but a conversation? Large Language Models (LLMs) offer a new paradigm for interacting with computers through text, but they lack shape reasoning capabilities. We introduce Textual Anatomy Encoding (TAE), a workflow that con
Despite recent advances, multimodal large language models continue to struggle with visual mathematical problem solving. Some recent works recognize that visual perception is a bottleneck in visual mathematical reasoning, but their solutions are limited to improving the extraction and interpretation…
The inherent difficulty and limited scalability of collecting manipulation data using multi-fingered robot hand hardware platforms have resulted in severe data scarcity, impeding research on data-driven dexterous manipulation policy learning. To address this challenge, we present a hand-agnostic man
Diffusion models have achieved outstanding success in image generation, yet their objectives are often limited to reconstruction, making it difficult to align with human preferences directly. Reinforcement learning (RL) offers a promising approach to address this by optimizing models using explicit
Large vision-language models have been shown to perform complex tasks. However, aligning language instructions with object visual information to enable general inference for robotic grasping poses a significant challenge. To tackle this issue, we introduce GraspControl, a method that leverages grasp…
Large vision-language models have been shown to perform complex tasks. However, aligning language instructions with object visual information to enable general inference for robotic grasping poses a significant challenge. To tackle this issue, we introduce GraspControl, a method that leverages grasp
As mixing services are increasingly being exploited by malicious actors for illicit transactions, mixing address association has emerged as a critical research task. A range of approaches have been explored, with graph-based models standing out for their ability to capture structural patterns in tra
Achieving an optimal balance between exploration and exploitation remains a fundamental challenge in reinforcement learning. This work revisits the exploration-exploitation dilemma through the lens of entropy, offering a novel perspective on this enduring problem. It establishes a theoretical connec
The primary challenge in incremental learning for deep neural networks (DNNs) lies in balancing memory stability with learning plasticity as they incrementally acquire new knowledge. Existing incremental learning methods typically require storing portions of old datasets or relying on specialized ne
Recent work has demonstrated the remarkable potential of Large Language Models (LLMs) in test-time scaling. By making models think before answering, they are able to achieve much higher accuracy with extra inference computation.However, in many real-world scenarios, models are used under time constr
Medical time-series analysis is crucial for the diagnosis and treatment of various diseases. As modern medical sensors evolve, the complexity and dimensionality of medical signals have increased, posing challenges for data labeling and classification. Self-supervised learning has emerged as a promis…
Humans achieve contact-rich dexterous grasping through the synergy of visual and tactile information. However, the high-dimensional action space of high DoF multi-fingered hands poses significant challenges to this operation. In this study, we address this complexity by controlling the robotic hand…
Generating grasps for a dexterous hand often requires numerous grasping annotations. However, annotating high DoF dexterous hand poses is quite challenging. Especially for functional grasps, requiring the hand to grasp the object in a specific pose to facilitate subsequent manipulations. This prompt
Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous h…
Generative models are gaining momentum in both academic and industrial applications driven by the availability of large-scale datasets, especially in tasks involving Image-to-Image Translation. Meanwhile, poor human perception of nighttime environment has led to a demand for translation from night-v…
Dynamic reconstruction in confocal non-line-of-sight imaging encounters great challenges since the dense raster-scanning manner limits the practical frame rate. A fewer pioneer works reconstruct high-resolution volumes from the under-scanning transient measurements but overlook temporal consistency…
There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency. For example, a rightsholder who currently wishes to know whether their copyrighted works have been used during training must convince the model pr…
Underwater adsorption is the key function of underwater robot operation, in order to complete the underwater wall fixation, underwater object capture and other operations. The underwater adhesion mechanism based on vibration control has the advantage of using the liquid viscosity to pull the object
To assist or replace human beings in completing various tasks, research on the functional grasp synthesis of dexterous hands with high degree-of-freedom (DoF) is necessary and challenging. The dexterous functional grasp requires not only that the grasp is stable but more importantly facilitates the
Successful grasp is an important and long-standing issue for robots to interact with the real world. Most recent studies have devoted more attention to stable grasp rather than functional grasp, which cannot guarantee task-oriented postgrasp manipulation. To achieve human-like functional grasp, a se
Fashion representation learning involves the analysis and understanding of various visual elements at different granularities and the interactions among them. Existing works often learn fine-grained fashion representations at the attribute-level without considering their relationships and inter-depe…
Multi-modal image matching is very challenging due to the significant diversities in visual appearance of different modal images. Typically, the existing well-performed methods mainly focus on learning invariant and discriminative features for measuring the relation between multi-modal image pairs.…
In this paper, we consider a new multi-armed bandit (MAB) framework motivated by three common complications in online recommender systems in practice: (i) the platform (learning agent) cannot sample an intended product directly and has to incentivize customers to select this product (e.g., promotion…
Road detection plays a fundamental role in the visual navigation system of autonomous vehicles. However, it's still challenging to achieve robust road detection in off-road scenarios due to their complicated road appearances and ambiguous road structures. Therefore, existing image-based road detecti…
Electro-adhesive clutches have become effective tools for variable stiffness functions in many robotic systems due to their light weight, high speed and strong brake force. In this paper, we present a novel, tubular design of an electro-adhesive clutch. Our clutch consists of flexible electrode shee…
"Fashion designs are rich in visual details associated with various visual attributes at both global and local levels. As a result, effective modeling and analyzing fashion requires fine-grained representations for individual attributes. In this work, we present a deep learning based online clusteri…
"Most state-of-the-art deep neural networks use static inference graphs, which makes it impossible for such networks to dynamically adjust the depth or width of the network according to the complexity of the input data. Different from these static models, depth-adaptive neural networks, e.g. the mul…
Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm. Nonetheless, virtually most prompt-based methods are token-level such as…
Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instance-level 6D pose estimation. However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects whic…
"Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp p…
Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need of prompt-style dataset to trigger specific behaviors of language models. In this paper, we address this gap by creating a prompt dataset with respect to occupations…
In minimally invasive surgery, miniaturisation and in situ adjustable stiffness of robotic manipulators are desired features. Previous research proposed a simple and effective tendon-driven curve-joint manipulator design using a variable neutral-line mechanism, which highly satisfies both criteria.…
In order to improve climbing performance of the robot in complex environments, the wheel-legged mechanism is gradually being widely used. In this letter, we simplify the previous eccentric paddle mechanism (ePaddle), and propose a 2-DOF ePaddle-based robot which integrates stability and maneuverabil
Though data augmentation has rapidly emerged as a key tool for optimization in modern machine learning, a clear picture of how augmentation schedules affect optimization and interact with optimization hyperparameters such as learning rate is nascent. In the spirit of classical convex optimization an…
Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision. To address the problem, we propose a self-supervised framework for hand mesh es…
In recent years, many dexterous robotic hands have been designed to assist or replace human hands in executing various tasks. But how to teach them to perform dexterous operations like human hands is still a challenging task. In this paper, we propose a grasp synthesis framework to make robots grasp…
In many real life situations, including job and loan applications, gatekeepers must make justified and fair real-time decisions about a person’s fitness for a particular opportunity. In this paper, we aim to accomplish approximate group fairness in an online stochastic decision-making process, where…
Chameleon tongues are extremely rapid, ultra-stretchable, compact, and can capture preys located multi-directionally. Various chameleon tongue inspired shooting manipulators have therefore been developed. Little work, however, has been studied on the multi-directional preying capability of chameleon
3D object detection is an important scene understanding task in autonomous driving and virtual reality. Approaches based on LiDAR technology have high performance, but LiDAR is expensive. Considering more general scenes, where there is no LiDAR data in the 3D datasets, we propose a 3D object detecti…
This paper focuses on the topic of vision based hand pose estimation from single depth map using convolutional neural network (CNN). Our main contributions lie in designing a new pose regression network architecture named CrossInfoNet. The proposed CrossInfoNet decomposes hand pose estimation task i…
In the above-named work, published in the July 2018 issue of the IEEE Robotics and Automation Letters, there is a missing citation in Sec. II-C. The citation should appear after the sentence "Since it is difficult to precisely model the inflation of the fabrics, we have the following assumptions" in
Traditional rigid robotic arms have been widely used in various industries. They own the merits of high force output, precise control, and high efficiency. However, a rigid robotic arm is seldom used by people in daily life, as the cost is usually high and, most importantly, its rigidity, large weig
The goal of this paper is to estimate the 3D coordinates of the hand joints from a single depth image. To give consideration to both the accuracy and the real time performance, we design a novel three-branch Convolutional Neural Networks named Hand Branch Ensemble network (HBE), where the three bran…
The recent developments of soft robots have inspired a variety of applications that require compliance for safety and softness for biomimetics. With such prevalence and advantages of soft robots, researchers are diverting their interests toward applications like micromanipulation of biological tissu
This letter presents a fully fabric-based bidirectional soft robotic glove designed to assist hand impaired patients in rehabilitation exercises and performing activities of daily living. The glove provides both active finger flexion and extension for hand assistance and rehabilitative training, thr
Silicone-based soft pneumatic actuator (SPA) is one of the key interests in soft robotic research. Currently, most of the SPAs bear a similar one-dimensional rod-like shape, regardless of their design and fabrication. There are few prototypes of SPAs with two-dimensional initial shapes, however, the…
Silicone-based bending soft pneumatic actuators (SPAs) have been very popular, since they provide solutions to many applications that require comfort and safety. However, their further utilization seems to be thwarted due to their limited force output. Force output can be the most important property
The planar rimless wheel (PRW) is a classic and simple passive dynamic mechanism to simulate biped walking, different simplified PRW models have respective descriptions and limited applications. This paper focus on constructing the general PRW model, and analyzing the intrinsic and mathematical rela…
Flexible robotic manipulators have been widely used in minimally invasive surgery (MIS) and many other applications requiring closer inspection and operation. Although a variety of manipulators enabled by different mechanism have been developed, few of them can preserve softness, thinness, and decen
This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial wei…
This paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden representations and adding supervision to early convolutional layers, DeepID2+ achieves new…
Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. Despite the significance of this problem, statistical characterization of t…
We analyze directed, unweighted graphs obtained from x_i∈\RR^d by connecting vertex i to j iff |x_i - x_j| < ε(x_i). Examples of such graphs include k-nearest neighbor graphs, where ε(x_i) varies from point to point, and, arguably, many real world graphs such as co-purchasing graphs. We ask whether…
To gain high mobility on challenging terrains, a mobile robot based on eccentric paddle mechanism (ePaddle) with locomotion versatility has been proposed. In this paper, a paddle-aided stair-climbing motion is presented for this ePaddle-based robot. The robot can roll on the stair as a traditional w…
Mandibular mobility plays a significant role in human daily lives, enabling food intake, respiration, speaking, and other oral activities. However, as for the patients suffering from mandibular mobility disorders, their mandible functions are deteriorated, which severely affects their quality of lif…