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Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots.
- Source :
-
International Journal of Robotics Research . Aug2019, Vol. 38 Issue 9, p1063-1097. 35p. - Publication Year :
- 2019
-
Abstract
- To overcome the obstructions imposed by high-dimensional bipedal models, we embed a stable walking motion in an attractive low-dimensional surface of the system's state space. The process begins with trajectory optimization to design an open-loop periodic walking motion of the high-dimensional model and then adding to this solution a carefully selected set of additional open-loop trajectories of the model that steer toward the nominal motion. A drawback of trajectories is that they provide little information on how to respond to a disturbance. To address this shortcoming, supervised machine learning is used to extract a low-dimensional state-variable realization of the open-loop trajectories. The periodic orbit is now an attractor of the low-dimensional state-variable model but is not attractive in the full-order system. We then use the special structure of mechanical models associated with bipedal robots to embed the low-dimensional model in the original model in such a manner that the desired walking motions are locally exponentially stable. The design procedure is first developed for ordinary differential equations and illustrated on a simple model. The methods are subsequently extended to a class of hybrid models and then realized experimentally on an Atrias-series 3D bipedal robot. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TRAJECTORY optimization
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 02783649
- Volume :
- 38
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- International Journal of Robotics Research
- Publication Type :
- Academic Journal
- Accession number :
- 137928319
- Full Text :
- https://doi.org/10.1177/0278364919859425