Using Motion Primitives in Probabilistic Sample-Based Planning for Humanoid Robots

  • Kris Hauser
  • Timothy Bretl
  • Kensuke Harada
  • Jean-Claude Latombe
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 47)


This paper presents a method of computing efficient and natural-looking motions for humanoid robots walking on varied terrain. It uses a small set of high-quality motion primitives (such as a fixed gait on flat ground) that have been generated offline. But rather than restrict motion to these primitives, it uses them to derive a sampling strategy for a probabilistic, sample-based planner. Results in simulation on several different terrains demonstrate a reduction in planning time and a marked increase in motion quality.


Humanoid Robot Planning Time Motion Quality Nominal Path Motion Primitive 
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

  • Kris Hauser
    • 1
  • Timothy Bretl
    • 1
  • Kensuke Harada
    • 2
  • Jean-Claude Latombe
    • 1
  1. 1.Computer Science DepartmentStanford University 
  2. 2.National Institute of Advanced Industrial Science and Technology (AIST)  

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