21 research outputs found
Generalization Properties and Implicit Regularization for Multiple Passes SGM
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 penalizations or constraints, the stability and
approximation properties of the algorithm can be controlled by tuning either
the step-size or the number of passes over the data. In this view, these
parameters can be seen to control a form of implicit regularization. Numerical
results complement the theoretical findings.Comment: 26 pages, 4 figures. To appear in ICML 201
Online semi-parametric learning for inverse dynamics modeling
This paper presents a semi-parametric algorithm for online learning of a
robot inverse dynamics model. It combines the strength of the parametric and
non-parametric modeling. The former exploits the rigid body dynamics equa-
tion, while the latter exploits a suitable kernel function. We provide an
extensive comparison with other methods from the literature using real data
from the iCub humanoid robot. In doing so we also compare two different
techniques, namely cross validation and marginal likelihood optimization, for
estimating the hyperparameters of the kernel function
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in
robotics. Several model classes including rigid body dynamics (RBD) models,
data-driven models and semiparametric models (which are a combination of the
previous two classes) are placed in a common framework. While model classes
used in the literature typically exploit joint velocities and accelerations,
which need to be approximated resorting to numerical differentiation schemes,
in this paper a new `derivative-free' framework is proposed that does not
require this preprocessing step. An extensive experimental study with real data
from the right arm of the iCub robot is presented, comparing different model
classes and estimation procedures, showing that the proposed `derivative-free'
methods outperform existing methodologies.Comment: 14 pages, 11 figure
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
Popular industrial robotic problems such as spray painting and welding
require (i) conditioning on free-shape 3D objects and (ii) planning of multiple
trajectories to solve the task. Yet, existing solutions make strong assumptions
on the form of input surfaces and the nature of output paths, resulting in
limited approaches unable to cope with real-data variability. By leveraging on
recent advances in 3D deep learning, we introduce a novel framework capable of
dealing with arbitrary 3D surfaces, and handling a variable number of unordered
output paths (i.e. unstructured). Our approach focuses on predicting smaller
path segments, which can be later concatenated to reconstruct long-horizon
paths. We extensively validate the proposed method in the context of robotic
spray painting by releasing PaintNet, the first public dataset of expert
demonstrations on free-shape 3D objects collected in a real industrial
scenario. A thorough experimental analysis demonstrates the capabilities of our
model to promptly predict smooth output paths that cover up to 95% of the
surface of previously unseen object instances. Furthermore, we show how models
learned from PaintNet capture relevant features which serve as a reliable
starting point to improve data and time efficiency when dealing with new object
categories
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
With the recent advances in machine learning, problems that traditionally
would require accurate modeling to be solved analytically can now be
successfully approached with data-driven strategies. Among these, computing the
inverse kinematics of a redundant robot arm poses a significant challenge due
to the non-linear structure of the robot, the hard joint constraints and the
non-invertible kinematics map. Moreover, most learning algorithms consider a
completely data-driven approach, while often useful information on the
structure of the robot is available and should be positively exploited. In this
work, we present a simple, yet effective, approach for learning the inverse
kinematics. We introduce a structured prediction algorithm that combines a
data-driven strategy with the model provided by a forward kinematics function
-- even when this function is misspecified -- to accurately solve the problem.
The proposed approach ensures that predicted joint configurations are well
within the robot's constraints. We also provide statistical guarantees on the
generalization properties of our estimator as well as an empirical evaluation
of its performance on trajectory reconstruction tasks.Comment: Accepted for publication in IEEE Robotics and Automation Letters
(2021) and presentation at IEEE International Conference on Robotics and
Automation (2021) Updated funding informatio
NYTRO: When Subsampling Meets Early Stopping
Early stopping is a well known approach to reduce the time complexity for performing training and model selection of large scale learning machines. On the other hand, memory/space (rather than time) complexity is the main constraint in many applications, and randomized subsampling techniques have been proposed to tackle this issue. In this paper we ask whether early stopping and subsampling ideas can be combined in a fruitful way. We consider the question in a least squares regression setting and propose a form of randomized iterative regularization based on early stopping and subsampling. In this context, we analyze the statistical and computational properties of the proposed method. Theoretical results are complemented and validated by a thorough experimental analysis