Zerrendatu honen arabera: egilea "167f5be90e3a86d014483f78029e8485"
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Data-efficient reinforcement learning for variable impedance control
Abu-Dakka, Fares J. (IEEE, 2024)One of the most crucial steps toward achieving human-like manipulation skills in robots is to incorporate compliance into the robot controller. Compliance not only makes the robot’s behaviour safe but also makes it more ... -
Digital Robot Judge: Building a Task-centric Performance Database of Real-World Manipulation With Electronic Task Boards
Abu-Dakka, Fares J. (IEEE, 2024)Robotics aims to develop manipulation skills approaching human performance. However, skill complexity is often over- or underestimated based on individual experience, and the real-world performance gap is difficult or ... -
EEG motor imagery classification: tangent space with gate-generated weight classifier
Abu-Dakka, Fares J. (MDPI, 2024)Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain–computer interface (BCI) technology has ... -
Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview
Abu-Dakka, Fares J. (Elsevier, 2024)Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. ... -
Learning periodic skills for robotic manipulation: Insights on orientation and impedance
Abu-Dakka, Fares J.; Saveriano, Matteo; Peternel, Luka (Elsevier, 2024)Many daily tasks exhibit a periodic nature, necessitating that robots possess the ability to execute them either alone or in collaboration with humans. A widely used approach to encode and learn such periodic patterns from ... -
A unified formulation of geometry-aware discrete dynamic movement primitives
Abu-Dakka, Fares J. (Elsevier, 2024)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 positions and forces from human demonstrations. ...
