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Online incremental learning of manipulation tasks for semi-autonomous teleoperation

Abstract:

We present an approach for online incremental learning of manipulation tasks. A Bayesian clustering algorithm is used to build an online hidden semi-Markov model (HSMM) that captures state transition and state duration probabilities of demonstrated motions. Motions are then generated by stochastically sampling from the learned model and tracked using an infinite-horizon linear quadratic regulator (LQR), with varying stiffness and damping characteristics learned from the demonstrations. Our ap...

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Publication status:
Not published
Peer review status:
Reviewed (Other)
Version:
Accepted manuscript

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
ORCID:
0000-0002-4371-4623
Tanwani, AK More by this author
Calinon, S More by this author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2016-10-05
Acceptance date:
2016-09-30
Pubs id:
pubs:822413
URN:
uri:b26c3659-7210-492a-a203-b71cecc68093
UUID:
uuid:b26c3659-7210-492a-a203-b71cecc68093
Local pid:
pubs:822413

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