- Abstract:
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We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to build models of manipulation motions as task-parametrized hidden semi-Markov models (TP-HSMM) that capture the spatiotemporal characteristics of demonstrated motions in a probabilistic representation. Motions are then executed autonomously usin...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
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(pdf, 3.4MB)
-
- Publisher copy:
- 10.1109/ICRA.2017.7989183
-
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- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2017-07-05
- Acceptance date:
- 2017-01-15
- DOI:
- ISSN:
-
1050-4729
- Pubs id:
-
pubs:726064
- URN:
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uri:efcbaa5a-03a4-4691-9a73-e5d491df0bbf
- UUID:
-
uuid:efcbaa5a-03a4-4691-9a73-e5d491df0bbf
- Local pid:
- pubs:726064
- ISBN:
- 9781509046331
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2017
- Notes:
- © 2017 IEEE.
Conference item
Supervisory teleoperation with online learning and optimal control
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