3 citations · 6 across the 6 of their papers we have counts for
6 papers
Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data
Bernardo Fichera, Zarko Ivkovic, Kjell Jorner +2
Uncertainty quantification for image data is dominated by complex deep learning methods, yet the field lacks an interpretable, mathematically grounded baseline. We propose Bayesian…
Hybrid Quadratic Programming -- Pullback Bundle Dynamical Systems Control
Bernardo Fichera, Aude Billard
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 st…
Learning Dynamical Systems Encoding Non-Linearity within Space Curvature
Bernardo Fichera, Aude Billard
Dynamical Systems (DS) are an effective and powerful means of shaping high-level policies for robotics control. They provide robust and reactive control while ensuring the stabilit…
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Noémie Jaquier, Michael C. Welle, Andrej Gams +6
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 situat…
Implicit Manifold Gaussian Process Regression
Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause +1
Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with h…
Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps
Bernardo Fichera, Aude Billard
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…