- Abstract:
-
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.
- Copyright:
- 2010 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- 1st Link
- Bibtex format
- @InProceedings{EDI-INF-RR-1372,
- 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 = {http://homepages.inf.ed.ac.uk/s0676829/papers/Icra2010Havoutis.pdf},
- }
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