首页|Recent Studies from Tsinghua University Add New Data to Robotics (Robotic Assembly Control Reconfiguration Based On Transfer Reinforcement Learning for Objects With Different Geometric Features)
Recent Studies from Tsinghua University Add New Data to Robotics (Robotic Assembly Control Reconfiguration Based On Transfer Reinforcement Learning for Objects With Different Geometric Features)
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NETL
NSTL
Elsevier
Fresh data on Robotics are presented in a new report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically.” The news correspondents obtained a quote from the research from Tsinghua University, “Since forcepose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method.”
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningReinforcement LearningRoboticsRobotsTsinghua University
NETL
NSTL
Elsevier
