activity
20192023
most citedDeep Model Predictive Variable Impedance Control

47 citations · 97 across the 9 of their papers we have counts for

collaborators

18 papers

cs.RO2023

Orientation Control with Variable Stiffness Dynamical Systems

Youssef Michel, Matteo Saveriano, Fares J. Abu-Dakka +1

Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional t…

cs.RO2023

SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features

Tran Nguyen Le, Fares J. Abu-Dakka, Ville Kyrki

Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interac…

cs.RO2023★ 14 cited

QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation

David Blanco-Mulero, Gokhan Alcan, Fares J. Abu-Dakka +1

Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these pr…

eess.SY2023★ 2 cited

Constrained Trajectory Optimization on Matrix Lie Groups via Lie-Algebraic Differential Dynamic Programming

Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki

Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and optimizing control policies on these manifolds is a fundamental problem. In this wo…

cs.RO2022★ 10 cited

Learning Deep Robotic Skills on Riemannian manifolds

Weitao Wang, Matteo Saveriano, Fares J. Abu-Dakka

In this paper, we propose RiemannianFlow, a deep generative model that allows robots to learn complex and stable skills evolving on Riemannian manifolds. Examples of Riemannian dat…

cs.RO2022

Geometric Reinforcement Learning For Robotic Manipulation

Naseem Alhousani, Matteo Saveriano, Ibrahim Sevinc +3

Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Lear…