activity
20222024
most citedDesign of Dynamics Invariant LSTM for Touch Based Human-UAV Interaction Detection

5 citations · 11 across the 11 of their papers we have counts for

collaborators

11 papers

cs.RO2024

SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains

Mubarak Yakubu, Yahya Zweiri, Ahmad Abubakar +3

Autonomous space rovers face significant challenges when navigating deformable and heterogeneous terrains due to variability in soil properties, which can lead to severe wheel slip…

cs.CV20241 cited

Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network

Yusra Alkendi, Rana Azzam, Sajid Javed +2

Moving object segmentation is critical to interpret scene dynamics for robotic navigation systems in challenging environments. Neuromorphic vision sensors are tailored for motion p…

cs.RO2024

Force-EvT: A Closer Look at Robotic Gripper Force Measurement with Event-based Vision Transformer

Qianyu Guo, Ziqing Yu, Jiaming Fu +3

Robotic grippers are receiving increasing attention in various industries as essential components of robots for interacting and manipulating objects. While significant progress has…

cs.RO20242 cited

A Novel Bioinspired Neuromorphic Vision-based Tactile Sensor for Fast Tactile Perception

Omar Faris, Mohammad I. Awad, Murana A. Awad +2

Tactile sensing represents a crucial technique that can enhance the performance of robotic manipulators in various tasks. This work presents a novel bioinspired neuromorphic vision…

eess.SY20241 cited

Physics-Informed LSTM-Based Delay Compensation Framework for Teleoperated UGVs

Ahmad Abubakar, Yahya Zweiri, AbdelGafoor Haddad +3

Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-indu…

cs.RO2023

Fuzzy Ensembles of Reinforcement Learning Policies for Robotic Systems with Varied Parameters

Abdel Gafoor Haddad, Mohammed B. Mohiuddin, Igor Boiko +1

Reinforcement Learning (RL) is an emerging approach to control many dynamical systems for which classical control approaches are not applicable or insufficient. However, the result…