Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space

  • Ioannis Havoutis
  • Subramanian Ramamoorthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5949)

Abstract

Motion synthesis for humanoid robot behaviours is made difficult by the combination of task space, joint space and kinodynamic constraints that define realisability. Solving these problems by general purpose methods such as sampling based motion planning has involved significant computational complexity, and has also required specialised heuristics to handle constraints. In this paper we propose an approach to incorporate specifications and constraints as a bias in the exploration process of such planning algorithms. We present a general approach to solving this problem wherein a subspace, of the configuration space and consisting of poses involved in a specific task, is identified in the form of a nonlinear manifold, which is in turn used to focus the exploration of a sampling based motion planning algorithm. This allows us to solve the motion planning problem so that we synthesize previously unseen paths for novel goals in a way that is strongly biased by known good or feasible paths, e.g., from human demonstration. We demonstrate this result with a simulated humanoid robot performing a number of bipedal tasks.

Keywords

Motion Planning Humanoid Robot Geodesic Distance Task Space Manifold Learning 
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 2010

Authors and Affiliations

  • Ioannis Havoutis
    • 1
  • Subramanian Ramamoorthy
    • 1
  1. 1.Institute of Perception, Action and Behaviour, School of InformaticsUniversity of EdinburghEdinburghUK

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