Collected demonstration trajectories.

<div><p>In the field of robotic arm trajectory imitation learning, Gaussian Mixture Models are widely used for their ability to capture the characteristics of complex trajectories. However, one major challenge in utilizing these models lies in the initialization process, particularly in...

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Bibliographic Details
Main Author:	Jingnan Yan (20727331) (author)
Other Authors:	Yue Wu (1262184) (author), Kexin Ji (18588025) (author), Cheng Cheng (68821) (author), Yili Zheng (20150407) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified model &# 8217 mean absolute error gaussian mixture regression gaussian mixture model proposed approach leverages experimental results demonstrate elbow method along value significantly impacts gaussian mixture models robotic arm trajectories resulting model parameters one major challenge robotic arms based value selection algorithm robotic arms proposed method selection based gaussian kernels results highlight models lies maximization algorithm complex trajectories random selection xlink "> widely used weight adjustments traditional methods suboptimal outcomes simulation experiments promising solution physical experiments paper proposes often lead means clustering initialization process exponential functions empirical knowledge correction terms accurately determine 15 %,
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Collected demonstration trajectories.

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