Motor synergies are an important concept in human motor control. Through the co-activation of multiple muscles, complex motion involving many degrees-of-freedom can be generated. However, leveraging this concept in robotics typically entails using human data that may be incompatible for the kinematics of the robot. In this paper, our goal is to enable a robot to identify synergies for low-dimensional control using trial-and-error only. We discuss how synergies can be learned through latent space policy search and introduce an extension of the algorithm for the re-use of previously learned synergies for exploration. The application of the algorithm on a bimanual manipulation task for the Baxter robot shows that performance can be increased by reusing learned synergies intra-task when learning to lift objects. But the reuse of synergies between two tasks with different objects did not lead to a significant improvement.
|Title of host publication
|IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Dec 13 2017
|2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: Sep 24 2017 → Sep 28 2017
|2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
|9/24/17 → 9/28/17
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Computer Science Applications