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Tanwani et al., 2021 - Google Patents

Sequential robot imitation learning from observations

Tanwani et al., 2021

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Document ID
16508147583234372986
Author
Tanwani A
Yan A
Lee J
Calinon S
Goldberg K
Publication year
Publication venue
The International Journal of Robotics Research

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Snippet

This paper presents a framework to learn the sequential structure in the demonstrations for robot imitation learning. We first present a family of task-parameterized hidden semi-Markov models that extracts invariant segments (also called sub-goals or options) from …
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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