Publications
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Journal articles
The impact of feature scaling in machine learning: Effects on regression and classification tasks
This paper addresses the critical lack of comprehensive studies on feature scaling by systematically evaluating 12 scaling techniques, including several less common transformations, across 14 different Machine Learning algorithms and 16 datasets for classification and regression tasks.
In this study, a wearable high-density lightmyography armband is proposed, and the offline and real-time grasp prediction schemes are compared in an attempt to deepen our understanding in real-time decoding employing lightmyography signals.
In this paper, we explore this new class of soft robotic grippers by proposing new designs and investigating their post-contact reconfiguration behaviour in a series of experiments covering grasping experiments and grasping force exertion measurement experiments.
In this work we compare the performance of EMG-based hand gesture decoding models developed using three learning approaches.
In this work, we introduce a new muscle-machine interfacing technique called lightmyography (LMG), that can be used to efficiently decode human hand gestures, motion, and forces from the detected contractions of the human muscles.
In this work, we compare various machine learning and feature extraction methods for the creation of EMG based control frameworks for dexterous robotic telemanipulation.
In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers.
In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands. The performance of this method is evaluated and compared with other well-known methods, employing the open-access Ninapro dataset.
Conference papers
Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation
This paper introduces a deep learning framework designed to enhance the grasping capabilities of quadrupeds equipped with arms, focusing on improved precision and adaptability.
A Synthetic Dataset for Manometry Recognition in Robotic Applications
This paper addresses the challenges of data scarcity and high acquisition costs for training robust object detection models in complex industrial environments, such as offshore oil platforms
Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach
This paper investigates the application of DRL for learning stable flight control to address the challenge of performing autonomous UAV navigation in confined spaces.
This paper introduces a vision-based teleoperation shared control framework designed to overcome real-time teleoperation limitations, providing intuitive, real-time control of a quadruped’s manipulator
This paper introduces a vision-based teleoperation shared control framework designed to overcome real-time teleoperation limitations, providing intuitive, real-time control of a quadruped’s manipulator
This study seeks to examine the impact of using different wavelengths of light in the decoding of hand postures using different machine learning methods.
MIHRaGe: A Mixed-Reality Interface for Human-Robot Interaction via Gaze-Oriented Control
This paper presents the MIHRAGe interface, an integrated system that combines gaze-tracking, robotic assistance, and a mixed-reality to create an immersive environment for controlling the robot using only eye movements.
In this paper, we present a new dataset capturing user interactions with a wide variety of everyday life objects using a fully actuated, human-like robot hand and an onboard camera.
This paper proposes a semi-autonomous framework for robotic telemanipulation that employs Electromyography (EMG) based motion decoding and potential fields to execute complex object stacking tasks with a dexterous robot arm-hand system.
In this work, we propose an efficient skill transfer methodology comprising intuitive interfaces, efficient optical tracking systems, and compliant control of robotic arm-hand systems.
In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation
In this paper, we propose an intuitive, affordances-oriented EMG-based telemanipulation framework for a robot arm-hand system that allows for dexterous control of the device.
In this paper, we propose an anthropomorphic, light-weight, and affordable prosthetic hand equipped with a five output, series elastic differential mechanism and an armband utilizing a new muscle machine interfacing method that is called Lightmyography (LMG).
In this study, we extend our previous work experimentally validating the efficiency of the LMG armband in classifying thirty-two different gestures from six participants using a deep learning technique called Temporal Multi-Channel Vision Transformers (TMC-ViT)
In this work, we focus on a comprehensive data collection and analysis of key attributes involved in the selection of grasping and manipulation strategies for the successful execution of kitchen tasks.
In this work, we focus on comparing human and robot performance in the execution of complex kitchen tasks, assessing the grasping and dexterous manipulation skills that are required.
Lightmyography Based Decoding of Human Intention Using Temporal Multi-Channel Transformers
In this work, we employ two novel deep learning techniques called Temporal Multi-Channel Transformer (TMC-T) and Temporal Multi-Channel Vision Transformer (TMC-ViT) for the classification of hand gestures based on the LMG data
In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands.
Redundant Robot Kinematics Error Analysis for Neurosurgical Procedures
This paper explores the kinematic relation between the robot configuration in joint space and both the robot tool center point (TCP) position resolution and the robot end-effector orientation resolution with the purpose of reducing error.
This paper presents a Deep Reinforcement Learning agent for a 4-wheeled rover in a multi-goal competition task, under the influence of noisy GPS measurements.
Workshop papers
Preprints
Evaluating Zero‑Shot and One‑Shot Adaptation of Small Language Models in Leader‑Follower Interaction
In this paper, we present the first benchmark of SLMs for leader–follower communication, introducing a novel dataset derived from a published database and augmented with synthetic samples to capture interaction-specific dynamics.
This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars.
A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation
In this paper, we collected 640 high-resolution images from a commercial farm spanning multiple growth stages, weed pressures, and lighting variations.
EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers
In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals.
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