首页|Researchers at Huazhong University of Science and Technology Release New Data on Robotics (An Active Semi-supervised Transfer Learning Method for Robot Pose Error Prediction and Compensation)
Researchers at Huazhong University of Science and Technology Release New Data on Robotics (An Active Semi-supervised Transfer Learning Method for Robot Pose Error Prediction and Compensation)
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NETL
NSTL
Elsevier
A new study on Robotics is now available. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from the Huazhong University of Science and Technology, “Existing methods suffer from high measurement costs, and limited applicability and accuracy. To address these issues, an active semi supervised transfer learning method (ASTL) is introduced. The pose error prediction problem was modelled as a transfer learning paradigm for the first time. It leverages the proposed multi-stage greedy sampling (MGS) for informed sample labelling combined with coarse calibration and semi supervised transfer learning (STL) to embed theoretical knowledge for globally accurate predictions. The proposed method is compared with other prediction and compensation approaches for four robotic motion tasks. It significantly reduces the time consumption by approximately 89.3% compared with direct measurements and achieves a maximum reduction of approximately 90% in robot pose errors.”
WuhanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsHuazhong University of Science and Technology
NETL
NSTL
Elsevier
