Publications de Bernardo Fichera

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Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. ...

Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept—reusing prior knowledge to learn in and from novel situations—is successfully leveraged by humans to handle novel situations. In recent ...

We propose to make a demonstration of the experimental setup that we have developed to measure human bimanual dexterity in watchmaking. These measurements and their analysis are used to develop robot controllers with similar dexterity. Next we explain the ...

Bernardo Fichera

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Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Geometric Learning: Leveraging differential geometry for learning and control

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Benchmark for Bimanual Robotic Manipulation of Semi-deformable Objects

Transfer learning in robotics: An upcoming breakthrough? A review of promises and challenges

Integrating Multisensory Information for Modeling Human Dexterous Bimanual Manipulation Skills