Task-relevant roadmaps: A framework for humanoid motion planning

Marijn Stollenga, Leo Pape, Mikhail Frank, Jurgen Leitner, Alexander Forster, Jurgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


To plan complex motions of robots with many degrees of freedom, our novel, very flexible framework builds task-relevant roadmaps (TRMs), using a new sampling-based optimizer called Natural Gradient Inverse Kinematics (NGIK) based on natural evolution strategies (NES). To build TRMs, NGIK iteratively optimizes postures covering task-spaces expressed by arbitrary task-functions, subject to constraints expressed by arbitrary cost-functions, transparently dealing with both hard and soft constraints. TRMs are grown to maximally cover the task-space while minimizing costs. Unlike Jacobian-based methods, our algorithm does not rely on calculation of gradients, making application of the algorithm much simpler. We show how NGIK outperforms recent related sampling algorithms. A video demo (http://youtu.be/N6x2e1Zf-yg) successfully applies TRMs to an iCub humanoid robot with 41 DOF in its upper body, arms, hands, head, and eyes. To our knowledge, no similar methods exhibit such a degree of flexibility in defining movements. © 2013 IEEE.
Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Number of pages7
StatePublished - Dec 1 2013
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2022-09-14


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