5 papers
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte +4
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on si…
Large-scale Kernel Methods and Applications to Lifelong Robot Learning
Raffaello Camoriano
As the size and richness of available datasets grow larger, the opportunities for solving increasingly challenging problems with algorithms learning directly from data grow at the…
Derivative-free online learning of inverse dynamics models
Diego Romeres, Mattia Zorzi, Raffaello Camoriano +2
This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semipara…
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi +2
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled a…
Generalization Properties and Implicit Regularization for Multiple Passes SGM
Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco
We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of…