首页|Findings from Peking University Reveals New Findings on Robotics (Autogeneration of Mission-oriented Robot Controllers Using Bayesian-based Koopman Operator)
Findings from Peking University Reveals New Findings on Robotics (Autogeneration of Mission-oriented Robot Controllers Using Bayesian-based Koopman Operator)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
A new study on Robotics is now available. According to news reporting originating in Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input-output mapping set, breaking through the barriers of the customization services.” Financial support for this research came from National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from Peking University, “However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC.”
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsPeking University
NETL
NSTL
IEEE
