首页|Shibaura Institute of Technology Researchers Update Current Data on Robotics (At tention-Based Grasp Detection With Monocular Depth Estimation)
Shibaura Institute of Technology Researchers Update Current Data on Robotics (At tention-Based Grasp Detection With Monocular Depth Estimation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on robotics are presented i n a new report. According to news reporting from Tokyo, Japan, by NewsRx journal ists, research stated, "Grasp detection plays a pivotal role in robotic manipula tion, allowing robots to interact with and manipulate objects in their surroundi ngs. Traditionally, this has relied on three-dimensional (3D) point cloud data a cquired from specialized depth cameras." Funders for this research include Jsps Kakenhi. Our news journalists obtained a quote from the research from Shibaura Institute of Technology: "However, the limited availability of such sensors in real-world scenarios poses a significant challenge. In many practical applications, robots operate in diverse environments where obtaining high-quality 3D point cloud data may be impractical or impossible. This paper introduces an innovative approach to grasp generation using color images, thereby eliminating the need for dedicat ed depth sensors. Our method capitalizes on advanced deep learning techniques fo r depth estimation directly from color images. Instead of relying on conventiona l depth sensors, our approach computes predicted point clouds based on estimated depth images derived directly from Red-Green-Blue (RGB) input data. To our know ledge, this is the first study to explore the use of predicted depth data for gr asp detection, moving away from the traditional dependence on depth sensors. The novelty of this work is the development of a fusion module that seamlessly inte grates features extracted from RGB images with those inferred from the predicted point clouds. Additionally, we adapt a voting mechanism from our previous work (VoteGrasp) to enhance robustness to occlusion and generate collision-free grasp s. Experimental evaluations conducted on standard datasets validate the effectiv eness of our approach, demonstrating its superior performance in generating gras p configurations compared to existing methods."
Shibaura Institute of TechnologyTokyoJapanAsiaEmerging TechnologiesMachine LearningNano-robotRobotics
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