225 citations · 586 across the 18 of their papers we have counts for
18 papers
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…
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…
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…
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…
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…
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…
Deep Model Predictive Variable Impedance Control
Akhil S Anand, Fares J. Abu-Dakka, Jan Tommy Gravdahl
The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to…
Learning Stable Robotic Skills on Riemannian Manifolds
Matteo Saveriano, Fares J. Abu-Dakka, Ville Kyrki
In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic…
A Unified Formulation of Geometry-aware Dynamic Movement Primitives
Fares J. Abu-Dakka, Matteo Saveriano, Ville Kyrki
Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint posit…
Periodic DMP formulation for Quaternion Trajectories
Fares J. Abu-Dakka, Matteo Saveriano, Luka Peternel
Imitation learning techniques have been used as a way to transfer skills to robots. Among them, dynamic movement primitives (DMPs) have been widely exploited as an effective and an…
Deformation-Aware Data-Driven Grasp Synthesis
Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka +1
Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful -- humans a…
Dynamic Movement Primitives in Robotics: A Tutorial Survey
Matteo Saveriano, Fares J. Abu-Dakka, Aljaz Kramberger +1
Biological systems, including human beings, have the innate ability to perform complex tasks in versatile and agile manner. Researchers in sensorimotor control have tried to unders…
Variable impedance control and learning -- A review
Fares J. Abu-Dakka, Matteo Saveriano
Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and pro…
Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements
Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka +1
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however…
A Novel Design of Soft Robotic Hand with a Human-inspired Soft Palm for Dexterous Grasping
Haihang Wang, Fares J. Abu-Dakka, Tran Nguyen Le +2
Soft robotic hands and grippers are increasingly attracting attention as a robotic end-effector. Compared with rigid counterparts, they are safer for human-robot and environment-ro…
Geometry-aware Dynamic Movement Primitives
Fares J. Abu-Dakka, Ville Kyrki
In many robot control problems, factors such as stiffness and damping matrices and manipulability ellipsoids are naturally represented as symmetric positive definite (SPD) matrices…
Towards Orientation Learning and Adaptation in Cartesian Space
Yanlong Huang, Fares J. Abu-Dakka, João Silvério +1
As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint s…
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
João Silvério, Yanlong Huang, Fares J. Abu-Dakka +2
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extra…