18 accepted papers
Magnetic microrobots hold great promise for biomedical applications. However, achieving flexible magnetic field adjustment with a magnetic actuation system (MAS) to actuate diverse microrobots remains a significant challenge. In this work, we propose an Electromagnetic-Permanent Magnet Actuation (EP…
Control of soft robots is considered one of the key elements in achieving their intelligence. However, it faces challenging problems such as nonlinear dynamics, highly deformable structures, and operation in unpredictable situations. Numerous methods have been proposed to overcome these challenges, …
Soft robotic grippers gently and safely manipulate delicate objects due to their inherent adaptability and softness. Limited by insufficient stiffness and imprecise force control, conventional soft grippers are not suitable for applications that require stable grasping force. In this work, we propos
Lexical tones play a crucial role in Sino-Tibetan languages. However, current phonetic fieldwork relies on manual effort, resulting in substantial time and financial costs. This is especially challenging for the numerous endangered languages that are rapidly disappearing, often compounded by limited…
The Segment-Anything Model (SAM) stands as a foundational framework for image segmentation. While it exhibits remarkable zero-shot generalization in typical scenarios, its advantage diminishes when applied to specialized domains like medical imagery and remote sensing. To address this limitation, th…
Large Language Models (LLMs) frequently hallucinate, impeding their reliability in mission-critical situations. One approach to address this issue is to provide citations to relevant sources alongside generated content, enhancing the verifiability of generations. However, citing passages accurately…
With infinite degrees of freedom, soft robots are expected to achieve dexterous and complex tasks, but this also puts forward higher requirements for their sensing capabilities. An important sensing task in soft robots is sensing their own deformation and current shape. Currently, most of the existi
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains lim…
Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacerbate…
When talking about soft robots, softness is considered the most important feature, which brings dexterity and safety in interactive tasks with humans and environments. Such softness sometimes limits the real application of soft robots because load capability and rigidity are widely needed on many oc
Soft robots with a well-balanced performance in terms of dexterity, accuracy, and payload have a great potential for application. Balancing safe human-robot interaction with operation performance enables the use of soft robot in biomedical fields, among others, such as surgery, rehabilitation and el
Soft robotics have demonstrated great advantages in assisting elderly/disabled people during daily tasks, owing to their highly dexterous motions and safe human-robot interactions. However, simultaneously controlling the position and force of soft robots is still a challenging task due to soft actua
Traditional normalization techniques (e.g., Batch Normalization and Instance Normalization) generally and simplistically assume that training and test data follow the same distribution. As distribution shifts are inevitable in real-world applications, well-trained models with previous normalization…
Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies in the image classification domain. However, two key points related to data augmentation remain uncovered by the curren…
Data augmentation is widely used to increase data variance in training deep neural networks. However, previous methods require either comprehensive domain knowledge or high computational cost. Can we learn data transformation automatically and efficiently with limited domain knowledge? Furthermore,…
Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes. Existing approaches predominantly focus on learning the proper mapping function for visual-semantic embedding, while neglecting the effect of learning…
Random data augmentation is a critical technique to avoid overfitting in training deep models. Yet, data augmentation and network training are often two isolated processes in most settings, yielding to a suboptimal training. Why not jointly optimize the two? We propose adversarial data augmentation…
In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localizatio…