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Fares J. Abu-Dakka

20 accepted papers

2026

Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning

Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba, Lingyun Chen, Shreenabh Agrawal, Fares J. Abu-Dakka +1
RA-L 2026

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S<inline-formula xmlns:mml="http://www.w3.org/1998/Math/Mat

Cited by 1Source ↗code ↗Scholar ↗
2025

Evaluating Human-Robot Skill Gaps in Electrical Circuit Inspection: A New Electronic Task Board for Benchmarking Manipulation

Peter So, Abdalla Swikir, Fares J. Abu-Dakka, Sami Haddadin
ICRA 2025

Robot manipulation researchers reference human performance as a goal for their work, however, human data is seldom present in robotics benchmarks. We introduce a real-world benchmark targeting manipulation skills for performing electrical circuit inspection with a multimeter using an Internet-connec

Cited by 0Source ↗Scholar ↗
2025

MeshDMP: Motion Planning on Discrete Manifolds Using Dynamic Movement Primitives

Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli, Daniele Fontanelli, Matteo Saveriano
ICRA 2025

An open problem in industrial automation is to reliably perform tasks requiring in-contact movements with complex workpieces, as current solutions lack the ability to seamlessly adapt to the workpiece geometry. In this paper, we propose a Learning from Demonstration approach that allows a robot mani

Cited by 4Source ↗Scholar ↗
2024

1 kHz Behavior Tree for Self-adaptable Tactile Insertion

Yansong Wu, Fan Wu, Lingyun Chen, Kejia Chen, Samuel Schneider, Lars Johannsmeier +4
ICRA 2024poster

Insertion is an essential skill for robots in both modern manufacturing and services robotics. In our previous study, we proposed an insertion skill framework based on forcedomain wiggle motion. The main limitation of this method lies in the robot’s inability to adjust its behavior according to chan…

Cited by 5Source ↗Scholar ↗
2024

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

Peter So, Rafael I. Cabral Muchacho, Robin Jeanne Kirschner, Abdalla Swikir, Luis Figueredo, Fares J. Abu-Dakka +1
ICRA 2024poster

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic lea…

Cited by 0Source ↗Scholar ↗
2024

Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements

Andrej Kruzliak, Jiri Hartvich, Shubhan P. Patni, Lukas Rustler, Jan Kristof Behrens, Fares J. Abu-Dakka +3
IROS 2024poster

This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects…

Cited by 2Source ↗code ↗Scholar ↗
2024

Safe Execution of Learned Orientation Skills with Conic Control Barrier Functions

Zheng Shen, Matteo Saveriano, Fares J. Abu-Dakka, Sami Haddadin
ICRA 2024poster

In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhibited by DSs may fall short in safety-critical task…

Cited by 1Source ↗Scholar ↗
2023

Orientation Control with Variable Stiffness Dynamical Systems

Youssef Michel, Matteo Saveriano, Fares J. Abu-Dakka, Dongheui Lee
IROS 2023poster

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 time-indexed tracking controllers. These approaches however mainly focused on positions, neglecting the orientation part…

Cited by 1Source ↗Scholar ↗
2023

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

David Blanco-Mulero, Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki
IROS 2023poster

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 primitives. Although existing solutions have tackled the parameterisation of pick and place locations, the effect of facto…

Cited by 9Source ↗Scholar ↗
2023

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

Tran Nguyen Le, Fares J. Abu-Dakka, Ville Kyrki
IROS 2023poster

Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for c…

Cited by 3Source ↗Scholar ↗
2022

A Bidirectional Soft Biomimetic Hand Driven by Water Hydraulic for Dexterous Underwater Grasping

Haihang Wang, He Xu, Fares J. Abu-Dakka, Ville Kyrki, Chen Yang, Xin Li +1
RA-L 2022

Soft robotics shows considerable promise for various underwater applications. Soft grippers as end-effectors are particularly useful for compliant and robust grasping compared to rigid mechanisms. In this work, we describe the design, fabrication and operation of a soft robotic hand driven by water

Cited by 34Source ↗Scholar ↗
2022

A Novel Simulation-Based Quality Metric for Evaluating Grasps on 3D Deformable Objects

Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka, Ville Kyrki
IROS 2022poster

Evaluation of grasps on deformable 3\mathrm{D}3\mathrm{D} objects is a little-studied problem, even if the applicability of rigid object grasp quality measures for deformable ones is an open question. A central issue with most quality measures is their dependence on contact points, which for deforma…

Cited by 8Source ↗Scholar ↗
2022

Editorial Variable Impedance Control and Learning in Complex Interaction Scenarios: Challenges and Opportunities

Fares J. Abu-Dakka, Matteo Saveriano, Meghan E. Huber, Thiago Boaventura
RA-L 2022

The papers in this special section focus on variable impedance control and learning in complex interaction applications. Increasingly, robots are expected to enter various application scenarios and interact with unknown and dynamically changing environments. More specifically, we are expecting robot

Cited by 1Source ↗Scholar ↗
2021

Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements

Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka, Ville Kyrki
RA-L 2021

Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In

Cited by 21Source ↗Scholar ↗
2019

Generalized Orientation Learning in Robot Task Space

Yanlong Huang, Fares J. Abu-Dakka, João Silvério, Darwin G. Caldwell
ICRA 2019poster

In the context of imitation learning, several approaches have been developed so as to transfer human skills to robots, with demonstrations often represented in Cartesian or joint space. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in man…

Cited by 22Source ↗Scholar ↗
2019

Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

João Silvério, Yanlong Huang, Fares J. Abu-Dakka, Leonel Rozo, Darwin G. Caldwell
IROS 2019poster

During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive mea…

Cited by 40Source ↗Scholar ↗
2017

Comparison of trajectory parametrization methods with statistical analysis for dynamic parameter identification of serial robot

Fares J. Abu-Dakka, Miguel Díaz-Rodríguez
IROS 2017poster

This paper introduces an approach for designing exciting trajectories for parameter identification of serial robots based on a combination of Fourier Series (FS) and Schroeder Phased Harmonic Sequence (SPHS). An initial estimation of the trajectory is designed for each link using SPHS. Afterwards, t…

Cited by 5Source ↗Scholar ↗
2016

A symbolic geometric formulation of branched articulated multibody systems based on graphs and lie groups

Juan A. Escalera, Fares J. Abu-Dakka, Mohamed Abderrahim
IROS 2016poster

In this article we present a symbolic closed-form matrix formulation to obtain the dynamic equations of branched articulated multibody systems (AMS)s. The proposed approach uses geometric mechanics based on Screw Theory and Lie groups. Both Lagrange's and Newton-Euler's equation of motion are derive…

Cited by 2Source ↗Scholar ↗