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Author(s): Ramos, R.
Duarte, M.
Oliveira, S. M.
Christensen, A. L.
Editor: Tuci, E., Giagkos, A., Wilson, M., and Hallam, J.
Date: 2016
Title: Evolving controllers for robots with multimodal locomotion
Volume: 9825
Pages: 340 - 351
Event title: 14th International Conference on Simulation of Adaptive Behavior, SAB 2016
ISBN: 978-3-319-43488-9
DOI (Digital Object Identifier): 10.1007/978-3-319-43488-9_30
Keywords: Evolutionary robotics
Multimodal locomotion
Navigation task
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.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:IT-CRI - Comunicações a conferências internacionais

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