9 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
Automatic Derivation of an Optimal Task Frame for Learning and Controlling Contact-Rich Tasks
Ali Mousavi Mohammadi, Maxim Vochten, Erwin Aertbeliën +1
In previous work on learning and controlling contact-rich tasks, the procedure for choosing a proper reference frame to express learned signals for the motion and the interaction w…
Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation
Pinhao Song, Pengteng Li, Erwin Aertbelien +1
We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion's onset, and (b) leveraging this predictor to facilitate sha…
Invariant Descriptors of Motion and Force Trajectories for Interpreting Object Manipulation Tasks in Contact
Maxim Vochten, Ali Mousavi Mohammadi, Arno Verduyn +3
Invariant descriptors of point and rigid-body motion trajectories have been proposed in the past as representative task models for motion recognition and generalization. Currently,…
Using Intent Estimation and Decision Theory to Support Lifting Motions with a Quasi-Passive Hip Exoskeleton
Thomas Callens, Vincent Ducastel, Joris De Schutter +1
This paper compares three controllers for quasi-passive exoskeletons. The Utility Maximizing Controller (UMC) uses intent estimation to recognize user motions and decision theory t…
Energy-Efficient Control of Cable Robots Exploiting Natural Dynamics and Task Knowledge
Boris Deroo, Erwin Aertbeliën, Wilm Decré +1
This paper focusses on the energy-efficient control of a cable-driven robot for tasks that only require precise positioning at few points in their motion, and where that accuracy c…