Abstract

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 full 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 start with a planar motion example to highlight the flexibility of our approach, and 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. In our trials, we achieve high repeatability, resulting to consistently successful autonomous hot-stabbing motions.

Bibtex reference

@inproceedings{Havoutis17ICRA,
  author="Havoutis, I. and Calinon, S.",
  title="Supervisory teleoperation with online learning and optimal control",
  booktitle="Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})",
  year="2017",
  month="May-June",
  address="Singapore",
  pages="1534--1540"
}

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