首页|Findings from South China University of Technology in the Area of Robotics Described (One-shot Sim-to-real Transfer Policy for Robotic Assembly Via Reinforcement Learning With Visual Demonstration)
Findings from South China University of Technology in the Area of Robotics Described (One-shot Sim-to-real Transfer Policy for Robotic Assembly Via Reinforcement Learning With Visual Demonstration)
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NETL
NSTL
Cambridge Univ Press
Research findings on Robotics are discussed in a new report. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Reinforcement learning (RL) has been successfully applied to a wealth of robot manipulation tasks and continuous control problems. However, it is still limited to industrial applications and suffers from three major challenges: sample inefficiency, real data collection, and the gap between simulator and reality.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Guangdong Basic and Applied Basic Research Foundation, Industrial Key Technologies R&D Program of Foshan.
GuangzhouPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningReinforcement LearningRobotRoboticsRobotsSouth China University of Technology
NETL
NSTL
Cambridge Univ Press
