Faezeh Haghverd — accepted AI-conference papers

2023

Deep Probabilistic Movement Primitives with a Bayesian Aggregator

Michael Przystupa, Faezeh Haghverd, Martin Jagersand, Samuele Tosatto

IROS 2023poster

Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of move-ments (reproducing m…

Cited by 5 Source ↗Scholar ↗

2023

Learning State Conditioned Linear Mappings for Low-Dimensional Control of Robotic Manipulators

Michael Przystupa, Kerrick Johnstonbaugh, Zichen Zhang, Laura Petrich, Masood Dehghan, Faezeh Haghverd +1

ICRA 2023poster

Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands o…

Cited by 3 Source ↗Scholar ↗