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Augmented Hierarchical Quadratic Programming for Adaptive Compliance Robot Control
2021
Zenodo
Today's robots are expected to fulfill different requirements originated from executing complex tasks in uncertain environments, often in collaboration with humans. To deal with this type of multi-objective control problem, hierarchical least-square optimization techniques are often employed, defining multiple tasks as objective functions, listed in hierarchical manner. The solution to the Inverse Kinematics problem requires to plan and constantly update the Cartesian trajectories. However, we
doi:10.5281/zenodo.4663753
fatcat:3tnn7h46jrer3nwtpcqnva42am