首页|Findings from Xinjiang University Broaden Understanding of Robotics (Bagging in Hidden Semi-Markov Model for handwriting robot trajectory generation)
Findings from Xinjiang University Broaden Understanding of Robotics (Bagging in Hidden Semi-Markov Model for handwriting robot trajectory generation)
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NETL
NSTL
Ios Press
Research findings on robotics are discussed in a new report. According to news reporting out of Urumqi, People's Republic of China, by NewsRx editors, research stated, “Handwriting robots as an application of Imitation Learning (IL).” Our news reporters obtained a quote from the research from Xinjiang University: “However, most methods have poor accuracy of trajectory generation under task constraints, and models are less robust to changes in demonstration data. This paper proposes an IL algorithm named Bagging in Hidden Semi-Markov Model (BHSMM). The demonstration data is first divided into several sub-datasets, and each sub-dataset is encoded into several basic learning models by Hidden Semi-Markov Models (HSMM). Then the relationship between the task constraint points and the basic learning models is used to derive the weights. Finally, the trajectories adapted to the task constraints are generated based on the weights. We conducted experiments on the handwritten dataset LASA and compared the accuracy error with the original HSMM method."
Xinjiang UniversityUrumqiPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRobotics
NETL
NSTL
Ios Press
