Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in four separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement, and 4) inserting a plug to verify the usability for precision-critical tasks in an experimental validation performed with non-expert users.
|Title of host publication||Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)|
|Publication status||Published - 2021|
|Event||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online at Prague, Czech Republic|
Duration: 27 Sep 2021 → 1 Oct 2021
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|City||Online at Prague|
|Period||27/09/21 → 1/10/21|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.