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Newton methods for k-order Markov Constrained Motion Problems
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This is a documentation of a framework for robot motion optimization that aims to draw on classical constrained optimization methods. With one exception the underlying algorithms are classical ones: Gauss-Newton (with adaptive step size and damping), Augmented Lagrangian, log-barrier, etc. The exception is a novel any-time version of the Augmented Lagrangian. The contribution of this framework is to frame motion optimization problems in a way that makes the application of these methods efficient, especially by defining a very general class of robot motion problems while at the same time introducing abstractions that directly reflect the API of the source code.
Forward citations
Cited by 3 Pith papers
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Regrasp Maps for Sequential Manipulation Planning
A grasp-signature map of object placements guides a KOMO-based planner with guessed regrasp sequences and placement constraints, improving success on long-horizon 2D regrasp problems.
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RAKOMO: Reachability-Aware K-Order Markov Path Optimization for Quadrupedal Loco-Manipulation
RAKOMO adds a neural-network-predicted leg reachability margin to KOMO path optimization, improving leg-joint feasibility in simulated quadruped loco-manipulation tasks.
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An Optimization-Augmented Control Framework for Single and Coordinated Multi-Arm Robotic Manipulation
A multi-modal controller that switches between optimization-based planning and force control completes simulated single-arm, bimanual, and four-arm manipulation tasks.
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