Abstract
central pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological and
control parameters directly on the physical robot.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2012 |
| ISBN (Print) | 9781467317283 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems - Madrid, Spain Duration: 17 May 2012 → 18 May 2012 |
Conference
| Conference | 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems |
|---|---|
| Country | Spain |
| City | Madrid |
| Period | 17/05/2012 → 18/05/2012 |
Keywords
- Actuators
- Biology
- Legged locomotion
- Manuals.
Cite this
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Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. / Christensen, David Johan; Larsen, Jørgen Christian; Stoy, Kasper.
Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE, 2012.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit
AU - Christensen, David Johan
AU - Larsen, Jørgen Christian
AU - Stoy, Kasper
PY - 2012
Y1 - 2012
N2 - This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of acentral pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological andcontrol parameters directly on the physical robot.
AB - This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of acentral pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological andcontrol parameters directly on the physical robot.
KW - Actuators
KW - Biology
KW - Legged locomotion
KW - Manuals.
U2 - 10.1109/EAIS.2012.6232806
DO - 10.1109/EAIS.2012.6232806
M3 - Article in proceedings
SN - 9781467317283
BT - Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems
PB - IEEE
ER -