Rapid Humanoid Motion Learning through Coordinated, Parallel Evolution

  • Marijn Stollenga
  • Jürgen Schmidhuber
  • Faustino Gomez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8575)


Planning movements for humanoid robots is still a major challenge due to the very high degrees-of-freedom involved. Most humanoid control frameworks incorporate dynamical constraints related to a task that require detailed knowledge of the robot’s dynamics, making them impractical as efficient planning. In previous work, we introduced a novel planning method that uses an inverse kinematics solver called Natural Gradient Inverse Kinematics (NGIK) to build task-relevant roadmaps (graphs in task space representing robot configurations that satisfy task constraints) by searching the configuration space via the Natural Evolution Strategies (NES) algorithm. The approach places minimal requirements on the constraints, allowing for complex planning in the task space. However, building a roadmap via NGIK is too slow for dynamic environments. In this paper, the approach is scaled-up to a fully-parallelized implementation where additional constraints coordinate the interaction between independent NES searches running on separate threads. Parallelization yields a 12× speedup that moves this promising planning method a major step closer to working in dynamic environments.


Robotics planning parallel search NES 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marijn Stollenga
    • 1
  • Jürgen Schmidhuber
    • 1
  • Faustino Gomez
    • 1
  1. 1.USI-SUPSIIDSIAManno-LuganoSwitzerland

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