17 accepted papers
Infrared and Visible Image Fusion (IVIF) produces enhanced images by fusing complementary visual information. However, most existing methods generate fixed outputs and cannot flexibly adapt to user-specific requirements. Recent text-guided approaches offer partial control but are limited to global o
Accurate uncertainty estimation is essential for reliable appearance-based gaze tracking. However, domain shifts between training and testing often lead to incorrect uncertainty estimates, which is a problem overlooked in existing uncertainty-aware gaze tracking models. To overcome this problem effi
Intent inferencing in teleoperation has been instrumental in aligning operator goals and coordinating actions with robotic partners. However, current intent inference methods often ignore subtle motion that can be strong indicators for a sudden change in intent. Specifically, we aim to tackle 1) if …
Image composition aims to seamlessly integrate a foreground object into a background, where generating realistic and geometrically accurate shadows remains a persistent challenge. While recent diffusion-based methods have outperformed GAN-based approaches, existing techniques, such as the diffusion-
In utilizing deep learning techniques for medical image segmentation, two types of imbalance issues are observed: inter-class imbalance between majority and minority classes and intra-class imbalance between easy and hard samples. However, existing loss functions typically confuse these issues, lead…
Existing deep learning-based Multi-focus Image Fusion (MFIF) methods often rely on loss functions derived from linear combinations of image quality metrics, leading to complexities in training and only marginal improvements in image quality. Recognizing this, our study identifies input space and sca…
Deep Reinforcement Learning (DRL) has shown its capability to solve the high degrees of freedom in control and the complex interaction with the object in the multi-finger dexterous in-hand manipulation tasks. Current DRL approaches lack behavior constraints during the learning process, leading to ag
Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Asymmetric Actor-Critic approaches are used for Sim2Real to reduce the rich idealized features in simulation to the accessible o…
In the context of medical image segmentation, complementary information among multi-modality images can improve segmentation performance. However, acquiring the complete multi-modality data in clinical settings is difficult. To tackle this problem, we propose a novel multi-modality knowledge distill…
Dexterous telemanipulation is crucial in advancing human-robot systems, especially in tasks requiring precise and safe manipulation. However, it faces significant challenges due to the physical differences between human and robotic hands, the dynamic interaction with objects, and the indirect contro…
In-hand manipulation is challenging for a multi-finger robotic hand due to its high degrees of freedom and complex interaction with the object. To enable in-hand manipulation, existing deep reinforcement learning-based approaches mainly focus on training a single robot-structure-specific policy thro…
One of the fundamental questions in shared control is how to allocate control power to the human and robot effectively. Conventional arbitration policies often define a uniform singular scalar for all 6 DOFs to blend human input and robot assistance. However, this singular scalar over-dominates some
Filter-based shared control aims to accept and augment an operator's ability to control a robot. Current solutions accept actions based on their direction aligning with the robot's optimal policy. These strategies reject a human's small corrective actions if they conflict with the robot's direction…
In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world environments, a human usually only has a general goal (e.g.,
While a human is tracking a moving object to prepare for later grasping, we naturally change our hand pose to generate optimal pre-grasp to avoid post-grasp adjustment. Robot hand controllers need dynamic pre-grasp planning capability, so they are not limited in dynamic tracking and catching tasks.…
Promoting a robot agent's autonomy level, which allows it to understand the human operator's intent and provide motion assistance to achieve it, has demonstrated great advantages to the operator's intent in teleoperation. However, the research has been limited to the target approaching process. We a…
To provide the elderly with appropriate and timely caregiving in activities of daily life (ADL), it is desired for robots to have the capability of intention awareness (IA). Different from existing context-specific intention awareness (CSIA) approaches which are based on a limited and passive knowle…