首页|Reports Summarize Robotics Findings from Tianjin University of Technology (Rlnn: a Force Perception Algorithm Using Reinforcement Learning)
Reports Summarize Robotics Findings from Tianjin University of Technology (Rlnn: a Force Perception Algorithm Using Reinforcement Learning)
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NETL
NSTL
Springer Nature
Research findings on Robotics are discussed in a new report. According to news reporting originating in Tianjin, People’s Republic of China, by NewsRx journalists, research stated, “Force perception is one of the important research branches in human-computer interaction and compliant control of contact-rich robots. Force perception without robot end-effector sensors has attracted increasing attention in recent years, but the existing research methods do not consider the feature selection of variables in the process, and redundant dimension will increase the force perception cost of robots.” Funders for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Tianjin University of Technology, “To solve the above problems, we propose a new algorithm framework of Reinforcement Learning Neural Network (RLNN), which can realize the force perception of contact-rich robot. And it has the advantages of dimensionality optimization of input variables and lightweight network structure. Feature selection experiment, network structure experiment and different variable prediction experiment are conducted respectively, which proves the feasibility of our proposed algorithm framework.”
TianjinPeople’s Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRobotRoboticsTianjin University of Technology
NETL
NSTL
Springer Nature
