As blind and low-vision (BLV) players engage more deeply with games, accessibility features have become essential. While some research has explored tools and strategies to enhance game accessibility, the specific expe...
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Stroke is one of the leading causes of disability worldwide. The efficacy of recovery is determined by a variety of factors, including patient adherence to rehabilitation programs. One way to increase patient adherenc...
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Robotic grasping poses a fundamental challenge in robotics, particularly when dealing with unknown objects. This skill has always been a focal point in robotics research due to its fundamental importance and inherent ...
Robotic grasping poses a fundamental challenge in robotics, particularly when dealing with unknown objects. This skill has always been a focal point in robotics research due to its fundamental importance and inherent complexity. Recent advances utilize state-of-the-art learning techniques, including deep learning and deep reinforcement learning, presenting their unique set of challenges. Training these models requires extensive data and computational resources, with the generalization to unknown objects presenting a significant obstacle for robots. This paper presents a comprehensive approach to unknown object robotic grasping, with a specific focus on top-down grasping actions. The process encompasses image preprocessing, the application of the Straight Skeleton (StSkel) method, and the systematic generation of grasp keypoints when applied to a selection of objects from the Dex-Net dataset. During the evaluation, the analysis incorporated crucial metrics, including the number of detected grasp keypoints for each object, the count of successfully generated grasp pairs, the overall success rate, and the success rate when considering the top 5 ranked grasp pairs. One of the notable strengths of this approach lies in its adaptability to a wide array of objects, ranging from simple shapes to complex ones. The proposed approach in this paper paves the way for automatically labeling grasping datasets, providing a valuable asset for developing auto-generating Deep-RL models. The StSkel method effectively captures essential structural information, making it suitable for real-world applications involving diverse objects. The experimental outcomes affirm the robustness and adaptability of the approach, as it most often achieved a high success rate in generating viable pairs of grasping points for most tested objects. Even complex objects yield impressive results, demonstrating the method’s potential for real-world applications in grasping unknown objects.
Guiding a user’s hand along a 3D path can help individuals avoid obstacles and manipulate everyday items with eyes-free. While prior work focused on haptic approaches using robots, auditory approaches for 3D path gui...
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In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount...
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Interactive image segmentation extremely accelerates the generation of high-quality annotation image datasets, which are the pillars of the applications of deep learning. However, these methods suffer from the insigni...
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Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (...
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Pre-training large transformer models with in-domain data improves domain adaptation and helps gain performance on the domain-specific downstream tasks. However, sharing models pre-trained on potentially sensitive dat...
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