Silvério, J, Huang, Y, Rozo, L et al. (1 more author) (2019) An Uncertainty-Aware Minimal Intervention Control Strategy Learned from Demonstrations. In: IROS 2018. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 Oct 2018, Madrid, Spain. IEEE , pp. 6065-6071.
Abstract
Motivated by the desire to have robots physically present in human environments, in recent years we have witnessed an emergence of different approaches for learning active compliance. Some of the most compelling solutions exploit a minimal intervention control principle, correcting deviations from a goal only when necessary, and among those who follow this concept, several probabilistic techniques have stood out from the rest. However, these approaches are prone to requiring several task demonstrations for proper gain estimation and to generating unpredictable robot motions in the face of uncertainty. Here we present a Programming by Demonstration approach for uncertainty-aware impedance regulation, aimed at making the robot compliant - and safe to interact with - when the uncertainty about its predicted actions is high. Moreover, we propose a data-efficient strategy, based on the energy observed during demonstrations, to achieve minimal intervention control, when the uncertainty is low. The approach is validated in an experimental scenario, where a human collaboratively moves an object with a 7-DoF torque-controlled robot.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Dec 2019 09:55 |
Last Modified: | 17 Dec 2019 13:18 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS.2018.8594220 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154627 |