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Title:Geodesic Trajectory Generation on Learnt Skill Manifolds
Authors: Ioannis Havoutis ; Subramanian Ramamoorthy
Date: 2010
Publication Title:2010 IEEE International Conference on Robotics and Automation
Publication Type:Conference Paper Publication Status:Submitted
Humanoid robots are appealing due to their inherent dexterity. However, these potential benefits may only be realized if the corresponding motion synthesis procedure is suitably flexible. This paper presents a flexible trajectory generation algorithm that utilizes a geometric representation of humanoid skills (e.g., walking) - in the form of skill manifolds. These manifolds are learnt from demonstration data that may be obtained from off-line optimization algorithms (or a human expert). We demonstrate that this model may be used to produce approximately optimal motion plans as geodesics over the manifold and that this allows us to effectively generalize from a limited training set. We demonstrate the effectiveness of our approach on a simulated 3-link planar arm, and then the more challenging example of a physical 19-DoF humanoid robot. We show that our algorithm produces a close approximation of the much more computationally intensive optimization procedure used to generate the data. This allows us to present experimental results for fast motion planning on a realistic -- variable step length, width and height -- walking task on a humanoid robot.
2010 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Ioannis Havoutis and Subramanian Ramamoorthy },
title = {Geodesic Trajectory Generation on Learnt Skill Manifolds},
book title = {2010 IEEE International Conference on Robotics and Automation},
year = 2010,
url = {},

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