Huang, Y, Silvério, J, Rozo, L et al. (1 more author) (2018) Hybrid Probabilistic Trajectory Optimization Using Null-Space Exploration. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). ICRA 2018, 21-25 May 2018, Brisbane, Australia. IEEE , pp. 7226-7232.
Abstract
In the context of learning from demonstration, human examples are usually imitated in either Cartesian or joint space. However, this treatment might result in undesired movement trajectories in either space. This is particularly important for motion skills such as striking, which typically imposes motion constraints in both spaces. In order to address this issue, we consider a probabilistic formulation of dynamic movement primitives, and apply it to adapt trajectories in Cartesian and joint spaces simultaneously. The probabilistic treatment allows the robot to capture the variability of multiple demonstrations and facilitates the mixture of trajectory constraints from both spaces. In addition to this proposed hybrid space learning, the robot often needs to consider additional constraints such as motion smoothness and joint limits. On the basis of Jacobian-based inverse kinematics, we propose to exploit robot null-space so as to unify trajectory constraints from Cartesian and joint spaces while satisfying additional constraints. Evaluations of hand-shaking and striking tasks carried out with a humanoid robot demonstrate the applicability of our approach.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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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 10:41 |
Last Modified: | 17 Dec 2019 13:40 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICRA.2018.8460550 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154622 |