Abstract
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 a
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.
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 |
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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 |
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Country | Spain |
City | Madrid |
Period | 17/05/2012 → 18/05/2012 |
Keywords
- Actuators
- Biology
- Legged locomotion
- Manuals.