33 research outputs found
Proprioceptive Robot Collision Detection through Gaussian Process Regression
This paper proposes a proprioceptive collision detection algorithm based on
Gaussian Regression. Compared to sensor-based collision detection and other
proprioceptive algorithms, the proposed approach has minimal sensing
requirements, since only the currents and the joint configurations are needed.
The algorithm extends the standard Gaussian Process models adopted in learning
the robot inverse dynamics, using a more rich set of input locations and an
ad-hoc kernel structure to model the complex and non-linear behaviors due to
frictions in quasi-static configurations. Tests performed on a Universal Robots
UR10 show the effectiveness of the proposed algorithm to detect when a
collision has occurred.Comment: Published at ACC 201
Kernel-based methods for Volterra series identification
Volterra series approximate a broad range of nonlinear systems. Their identification is challenging due to the curse of dimensionality: the number of model parameters grows exponentially with the complexity of the input-output response. This fact limits the applicability of such models and has stimulated recently much research on regularized solutions. Along this line, we propose two new strategies that use kernel-based methods. First, we introduce the multiplicative polynomial kernel (MPK). Compared to the standard polynomial kernel, the MPK is equipped with a richer set of hyperparameters, increasing flexibility in selecting the monomials that really influence the system output. Second, we introduce the smooth exponentially decaying multiplicative polynomial kernel (SEDMPK), that is a regularized version of MPK which requires less hyperparameters, allowing to handle also high-order Volterra series. Numerical results show the effectiveness of the two approaches. (C) 2021 Elsevier Ltd. All rights reserved
Robot kinematic structure classification from time series of visual data
In this paper we present a novel algorithm to solve the robot kinematic
structure identification problem. Given a time series of data, typically
obtained processing a set of visual observations, the proposed approach
identifies the ordered sequence of links associated to the kinematic chain, the
joint type interconnecting each couple of consecutive links, and the input
signal influencing the relative motion. Compared to the state of the art, the
proposed algorithm has reduced computational costs, and is able to identify
also the joints' type sequence
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
In this paper, we propose a black-box model based on Gaussian process
regression for the identification of the inverse dynamics of robotic
manipulators. The proposed model relies on a novel multidimensional kernel,
called \textit{Lagrangian Inspired Polynomial} (\kernelInitials{}) kernel. The
\kernelInitials{} kernel is based on two main ideas. First, instead of directly
modeling the inverse dynamics components, we model as GPs the kinetic and
potential energy of the system. The GP prior on the inverse dynamics components
is derived from those on the energies by applying the properties of GPs under
linear operators. Second, as regards the energy prior definition, we prove a
polynomial structure of the kinetic and potential energy, and we derive a
polynomial kernel that encodes this property. As a consequence, the proposed
model allows also to estimate the kinetic and potential energy without
requiring any label on these quantities. Results on simulation and on two real
robotic manipulators, namely a 7 DOF Franka Emika Panda and a 6 DOF MELFA
RV4FL, show that the proposed model outperforms state-of-the-art black-box
estimators based both on Gaussian Processes and Neural Networks in terms of
accuracy, generality and data efficiency. The experiments on the MELFA robot
also demonstrate that our approach achieves performance comparable to
fine-tuned model-based estimators, despite requiring less prior information
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning
In this paper, we propose to estimate the forward dynamics equations of
mechanical systems by learning a model of the inverse dynamics and estimating
individual dynamics components from it. We revisit the classical formulation of
rigid body dynamics in order to extrapolate the physical dynamical components,
such as inertial and gravitational components, from an inverse dynamics model.
After estimating the dynamical components, the forward dynamics can be computed
in closed form as a function of the learned inverse dynamics. We tested the
proposed method with several machine learning models based on Gaussian Process
Regression and compared them with the standard approach of learning the forward
dynamics directly. Results on two simulated robotic manipulators, a PANDA
Franka Emika and a UR10, show the effectiveness of the proposed method in
learning the forward dynamics, both in terms of accuracy as well as in opening
the possibility of using more structured~models
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
In this paper, we present a Model-Based Reinforcement Learning algorithm
named Monte Carlo Probabilistic Inference for Learning COntrol (MC-PILCO). The
algorithm relies on Gaussian Processes (GPs) to model the system dynamics and
on a Monte Carlo approach to estimate the policy gradient. This defines a
framework in which we ablate the choice of the following components: (i) the
selection of the cost function, (ii) the optimization of policies using
dropout, (iii) an improved data efficiency through the use of structured
kernels in the GP models. The combination of the aforementioned aspects affects
dramatically the performance of MC-PILCO. Numerical comparisons in a simulated
cart-pole environment show that MC-PILCO exhibits better data-efficiency and
control performance w.r.t. state-of-the-art GP-based MBRL algorithms. Finally,
we apply MC-PILCO to real systems, considering in particular systems with
partially measurable states. We discuss the importance of modeling both the
measurement system and the state estimators during policy optimization. The
effectiveness of the proposed solutions has been tested in simulation and in
two real systems, a Furuta pendulum and a ball-and-plate.Comment: Submitted to IEEE Transactions on Robotic
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully
applied to (semi-) autonomous driving problems and has proven to be a very
promising technique. However, accurate control models for real vehicles could
require costly and time-demanding specific measurements. To address this
problem, the exploitation of system data to complement or derive the prediction
model of the NMPC has been explored, employing learning dynamics approaches
within Learning-based NMPC (LbNMPC). Its application to the automotive field
has focused on discrete grey-box modeling, in which a nominal dynamics model is
enhanced by the data-driven component. In this manuscript, we present an LbNMPC
controller for a real go-kart based on a continuous black-box model of the
accelerations obtained by Gaussian Processes. We show the effectiveness of the
proposed approach by testing the controller on a real go-kart vehicle,
highlighting the approximation steps required to get an exploitable GP model on
a real-time application.Comment: Accepted in IEEE Transaction on Control System Technology as Full
Paper for SI: State-of-the-art Applications of Model Predictive Control. 12
pages, 20 figure