Please use this identifier to cite or link to this item:
Oliveira, S. M.
Christensen, A. L.
|Editor:||Tuci, E., Giagkos, A., Wilson, M., and Hallam, J.|
|Title:||Evolving controllers for robots with multimodal locomotion|
|Pages:||340 - 351|
|Event title:||14th International Conference on Simulation of Adaptive Behavior, SAB 2016|
|DOI (Digital Object Identifier):||10.1007/978-3-319-43488-9_30|
|Abstract:||Animals have inspired numerous studies on robot locomotion, but the problem of how autonomous robots can learn to take advantage of multimodal locomotion remains largely unexplored. In this paper, we study how a robot with two different means of locomotion can effective learn when to use each one based only on the limited information it can obtain through its onboard sensors. We conduct a series of simulation-based experiments using a task where a wheeled robot capable of jumping has to navigate to a target destination as quickly as possible in environments containing obstacles. We apply evolutionary techniques to synthesize neural controllers for the robot, and we analyze the evolved behaviors. The results show that the robot succeeds in learning when to drive and when to jump. The results also show that, compared with unimodal locomotion, multimodal locomotion allows for simpler and higher performing behaviors to evolve.|
|Access type:||Open Access|
|Appears in Collections:||IT-CRI - Comunicações a conferências internacionais|
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