首页|New Findings from Shandong University in the Area of Robotics Reported (Robot Skill Generalization: Feature-selected Adaptation Transfer for Peg-in-hole Assembly)
New Findings from Shandong University in the Area of Robotics Reported (Robot Skill Generalization: Feature-selected Adaptation Transfer for Peg-in-hole Assembly)
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NETL
NSTL
IEEE
Data detailed on Robotics have been presented. According to news reporting originating from Jinan, People’s Republic of China, by NewsRx correspondents, research stated, “Skill generalization across different tasks is currently a challenging task for robots. As for recent works based on robot learning, substantial environmental interaction costs or abundant expert data are usually needed, thus causing great harm to the robot or the operating object.” Financial supporters for this research include Guangdong Key Research and Development Program, National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Shandong University, “In this article, featureselected adaptation transfer is proposed, aiming at accelerating the network learning process, and reducing the harm caused by the interaction process. Based on the domain adaptation, the source domain data with small maximum mean discrepancy to the target domain are extracted to pretrain the target domain policy. By extracting the shared features of the source domain and the target domain, the knowledge transfer between old task and new task is realized. Moreover, the data, more favorable to the target domain, are selected to update the network and further improve the stability of network training.”
JinanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsShandong University
NETL
NSTL
IEEE
