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HMDP: A New Protocol for Motion Pattern Generation Towards Behavior Abstraction

  • Norbert Michael Mayer
  • Joschka Boedecker
  • Kazuhiro Masui
  • Masaki Ogino
  • Minoru Asada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5001)

Abstract

The control of more than 20 degrees of freedom in real-time is one challenge of humanoid robotics. The control architecture of an autonomous humanoid robot often consists of two parts, namely a real-time part that has direct access to the motors or RC servos, and a non-real-time part, that controls the higher-level behaviors and sensory information processing such as vision and touch. As a result motion patterns are developed separately from the other parts of the robots behavior. In research, particularly when including developmental processes, it is often necessary that the design or the evolution of motion patterns is integrated in the overall development of the robot’s behavior. This is indeed one of the main principles of the embodied intelligence paradigm. The main aim of this work is to define a flexible way of describing motion patterns that can be passed to the motion controller which in turn executes them in real-time. As a result, the Harmonic Motion Description Protocol (HMDP) is presented. It allows the motions to be described as vectors of coefficients of harmonic motion splines. The motion splines are expressed as human-readable ASCII strings that can be passed as a motion stream. Flexibility is achieved by implementing the principle of superposition of several motion patterns. In this way also closed loop control is achievable in principle. Moreover, the HMDP can be implemented into the (deleted for blind review) project of the 3D soccer simulation league as a standard way to communicate motion patterns between the agent and the simulation interface and/or real humanoid robots.

Keywords

Motion Pattern Humanoid Robot Real Robot Motion Controller Motor Controller 
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 2008

Authors and Affiliations

  • Norbert Michael Mayer
    • 1
  • Joschka Boedecker
    • 1
  • Kazuhiro Masui
    • 1
  • Masaki Ogino
    • 1
  • Minoru Asada
    • 1
  1. 1.Dept. of Adaptive Machine Systems, Graduate School of EngineeringOsaka University, Osaka, Japan and, Asada S.I. Project, ERATO JSTOsakaJapan

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