首页|Reports Summarize Robotics Study Results from Northeastern Uni- versity (Sg-grasp: Semantic Segmentation Guided Robotic Grasp Oriented To Weakly Textured Objects Based On Visual Perception Sensors)
Reports Summarize Robotics Study Results from Northeastern Uni- versity (Sg-grasp: Semantic Segmentation Guided Robotic Grasp Oriented To Weakly Textured Objects Based On Visual Perception Sensors)
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IEEE
2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting originating in Shenyang, People’s Republic of China, by NewsRx journalists, research stated, “Weakly textured objects are frequently manipulated by industrial and domestic robots, and the most common two types are transparent and reflective objects; however, their unique visual properties present challenges even for advanced grasp detection algorithms. Many existing algorithms heavily rely on depth information, which is not accurately provided by ordinary red-green-blue and depth (RGB-D) sensors for transparent and reflective objects.” Funders for this research include National Natural Science Foundation of China (NSFC), Chunhui Plan Cooperative Project of Ministry of Education, Ministry of Education, China - 111 Project.
ShenyangPeople’s Republic of ChinaAsiaEmerging Tech- nologiesMachine LearningNano-robotRoboticsRobotsNortheastern University
NETL
NSTL
IEEE
