Large-Scale Hierarchical Decision-Making in Robot Control via ADMM
Large-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.
The paper proposes an ADMM-based solver for sparse hierarchical nonlinear programming in robot control, reporting up to 23.5% reduced computation times versus interior-point methods in low- to moderate-accuracy regimes; claims of whole-body dynamic control extension are presented as part of the work's scope.
Source: arXiv Robotics — research abstracts · Read original article ↗
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e solver efficiently handles separable decision functions by evaluating linear algebra expressions in the appropriate order. Results in large-scale kinematic and dynamic control show reduced computation times of up to 23.5% compared with a structure-exploiting interior-point method in low- to moderate-accuracy regimes. Furthermore, hierarchical decision-making is extended to whole-body dynamic control for humanoid wa
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Source：arXiv Robotics — research abstracts · arxiv.org