Large-Scale Hierarchical Decision-Making in Robot Control via ADMM
Overview
This research introduces an Alternating Direction Method of Multipliers (ADMM) solver for sparse hierarchical nonlinear programming (SH-NLP) to improve efficiency in robot kinematic and dynamic control. The authors report computation time reductions of up to 23.5% compared with interior-point methods in low- to moderate-accuracy regimes and extend the approach to whole-body dynamic control for humanoid walking.
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- arXiv Robotics — research abstractsSignalLarge-Scale Hierarchical Decision-Making in Robot Control via ADMM
This research introduces an Alternating Direction Method of Multipliers (ADMM) solver for sparse hierarchical nonlinear programming (SH-NLP) to improve efficiency in robot kinematic and dynamic control. The authors report computation time reductions of up to 23.5% compared with interior-point methods in low- to moderate-accuracy regimes and extend the approach to whole-body dynamic control for humanoid walking.
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