Silvério, J, Huang, Y, Rozo, L et al. (2 more authors) (2019) Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IROS 2018, 01-05 Oct 2018, Madrid, Spain. IEEE
Abstract
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7- DoF torque-controlled manipulators, with tasks that require the consideration of different controllers to be properly executed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, 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: |
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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 10:05 |
Last Modified: | 17 Dec 2019 13:20 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IROS.2018.8594103 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154626 |