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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Robot. AI | doi: 10.3389/frobt.2019.00105

Guided Stochastic Optimization for Motion Planning

 Bence Magyar1*,  Nikolaos Tsiogkas1,  Bruno Brito2, Mayank Patel3, David Lane1 and  Sen Wang1
  • 1School of Engineering and Physical Sciences, Heriot-Watt University, United Kingdom
  • 2Delft University of Technology, Netherlands
  • 3Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA, Germany

Learning from Demonstration (LfD) is a family of methods used to teach robots specific tasks. It is used to assist them with the increasing difficulty of performing manipulation tasks in a scalable manner. The state-of-the-art in collaborative robots allows for simple LfD approaches that can handle limited parameter changes of a task. These methods however typically approach the problem from a control perspective and therefore are tied to specific robot platforms. In contrast, this paper proposes a novel motion planning approach that combines the benefits of LfD approaches with generic motion planning that can provide robustness to the planning process as well as scaling task learning both in number of tasks and number of robot platforms. Specifically, it introduces Dynamical Movement Primitives (DMPs) based learning from demonstration as initial trajectories for the Stochastic Optimization for Motion Planning (STOMP) framework. This allows for successful task execution even when the task parameters and the environment change. Moreover, the proposed approach allows for skill transfer between robots. In this case a task is demonstrated to one robot via kinesthetic teaching and can be successfully executed by a different robot. The proposed approach, coined Guided Stochastic Optimization for Motion Planning (GSTOMP) is evaluated extensively using two different manipulator systems in simulation and in real conditions. Results show that GSTOMP improves task success compared to simple LfD approaches employed by the state-of-the-art collaborative robots. Moreover, it is shown that transferring skills is feasible and with good performance. Finally, the proposed approach is compared against a plethora of state-of-the-art motion planners. The results show that the motion planning performance is comparable or better than the state-of-the-art.

Keywords: motion planning, Trajectory optimization, Dynamical movement primitives, Manipulator motion planning, Learning from Demonstration (LFD)

Received: 07 Jun 2019; Accepted: 10 Oct 2019.

Copyright: © 2019 Magyar, Tsiogkas, Brito, Patel, Lane and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Bence Magyar, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom,