丛书名:Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
ISBN:978-3-319-52277-7
卷排序:10125
文摘
In this work a different probabilistic motor primitive parameterization is proposed using latent force models (LFMs). The sequential composition of different motor primitives is also addressed using hidden Markov models (HMMs) which allows to capture the redundancy over dynamics by using a limited set of hidden primitives. The capability of the proposed model to learn and identify motor primitive occurrences over unseen movement realizations is validated using synthetic and motion capture data.