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Alessandra Bernardini

3 accepted papers

2025

SLIM: A Symmetric, Low-Inertia Manipulator for Constrained, Contact-Rich Spaces

RA-L 2025

Operation in constrained and cluttered spaces poses a challenge for robotic manipulators, in part due to their bulky link geometry and kinematic limitations in comparison to human hands and arms. To address these limitations, we introduce SLIM, a custom end-effector consisting of a bidirectional han

Cited by 0SourceScholar
2023

Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control

RA-L 2023

Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, ho

Cited by 9SourceScholar
2022

sEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control

RA-L 2022

One of the major challenges in robotics consists in developing successful control strategies for robotic grasping devices. In this scenario, one of the most interesting approaches regards the exploitation of surface electromyography(sEMG). In this work, we propose a novel sEMG-based <italic xmlns:mm

Cited by 12SourceScholar