TY - GEN
T1 - Task-relevant roadmaps: A framework for humanoid motion planning
AU - Stollenga, Marijn
AU - Pape, Leo
AU - Frank, Mikhail
AU - Leitner, Jurgen
AU - Forster, Alexander
AU - Schmidhuber, Jurgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/6697192/
UR - http://www.scopus.com/inward/record.url?scp=84893804385&partnerID=8YFLogxK
U2 - 10.1109/IROS.2013.6697192
DO - 10.1109/IROS.2013.6697192
M3 - Conference contribution
SN - 9781467363587
SP - 5772
EP - 5778
BT - IEEE International Conference on Intelligent Robots and Systems
ER -