Supervisory teleoperation with online learning and optimal control
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-pa...
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2017
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| _version_ | 1797102867024183296 |
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| author | Havoutis, I Calinon, S |
| author_facet | Havoutis, I Calinon, S |
| author_sort | Havoutis, I |
| collection | OXFORD |
| description | 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 using an optimal controller, namely a model predictive control (MPC) approach in a receding horizon fashion. This way the remote system locally closes a high-frequency control loop that robustly handles noise and dynamically changing environments. Our system automates common and recurring tasks, allowing the operator to focus only on the tasks that genuinely require human intervention. We demonstrate how our solution can be used for a hot-stabbing motion in an underwater teleoperation scenario. We evaluate the performance of the system over multiple trials and compare with a state-of-the-art approach. We report that our approach generalizes well with only a few demonstrations, accurately performs the learned task and adapts online to dynamically changing task conditions. |
| first_indexed | 2024-03-07T06:11:54Z |
| format | Conference item |
| id | oxford-uuid:efcbaa5a-03a4-4691-9a73-e5d491df0bbf |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:11:54Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:efcbaa5a-03a4-4691-9a73-e5d491df0bbf2022-03-27T11:42:53ZSupervisory teleoperation with online learning and optimal controlConference itemhttp://purl.org/coar/resource_type/c_5794uuid:efcbaa5a-03a4-4691-9a73-e5d491df0bbfSymplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2017Havoutis, ICalinon, S 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 using an optimal controller, namely a model predictive control (MPC) approach in a receding horizon fashion. This way the remote system locally closes a high-frequency control loop that robustly handles noise and dynamically changing environments. Our system automates common and recurring tasks, allowing the operator to focus only on the tasks that genuinely require human intervention. We demonstrate how our solution can be used for a hot-stabbing motion in an underwater teleoperation scenario. We evaluate the performance of the system over multiple trials and compare with a state-of-the-art approach. We report that our approach generalizes well with only a few demonstrations, accurately performs the learned task and adapts online to dynamically changing task conditions. |
| spellingShingle | Havoutis, I Calinon, S Supervisory teleoperation with online learning and optimal control |
| title | Supervisory teleoperation with online learning and optimal control |
| title_full | Supervisory teleoperation with online learning and optimal control |
| title_fullStr | Supervisory teleoperation with online learning and optimal control |
| title_full_unstemmed | Supervisory teleoperation with online learning and optimal control |
| title_short | Supervisory teleoperation with online learning and optimal control |
| title_sort | supervisory teleoperation with online learning and optimal control |
| work_keys_str_mv | AT havoutisi supervisoryteleoperationwithonlinelearningandoptimalcontrol AT calinons supervisoryteleoperationwithonlinelearningandoptimalcontrol |