AI in Locomotion: Challenges and Perspectives of Underactuated Robots

  • Fumiya Iida
  • Rolf Pfeifer
  • André Seyfarth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4850)


This article discusses the issues of adaptive autonomous navigation as a challenge of artificial intelligence. We argue that, in order to enhance the dexterity and adaptivity in robot navigation, we need to take into account the decentralized mechanisms which exploit physical system-environment interactions. In this paper, by introducing a few underactuated locomotion systems, we explain (1) how mechanical body structures are related to motor control in locomotion behavior, (2) how a simple computational control process can generate complex locomotion behavior, and (3) how a motor control architecture can exploit the body dynamics through a learning process. Based on the case studies, we discuss the challenges and perspectives toward a new framework of adaptive robot control.


Biped Robot Body Dynamic Rough Terrain Quadruped Robot Passive Joint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fumiya Iida
    • 1
    • 2
  • Rolf Pfeifer
    • 2
  • André Seyfarth
    • 3
  1. 1.Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139USA
  2. 2.Artificial Intelligence Laboratory, Department of Informatics, University of Zurich, Andreasstrasse 15, CH-8050 ZurichSwitzerland
  3. 3.Locomotion Laboratory, University of Jena, Dornburger Strasse 23, D-07743 JenaGermany

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