8 research outputs found
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
Learning robot inverse dynamics using sparse online Gaussian process with forgetting mechanism
Online Gaussian processes (GPs), typically used for learning models from
time-series data, are more flexible and robust than offline GPs. Both local and
sparse approximations of GPs can efficiently learn complex models online. Yet,
these approaches assume that all signals are relatively accurate and that all
data are available for learning without misleading data. Besides, the online
learning capacity of GPs is limited for high-dimension problems and long-term
tasks in practice. This paper proposes a sparse online GP (SOGP) with a
forgetting mechanism to forget distant model information at a specific rate.
The proposed approach combines two general data deletion schemes for the basis
vector set of SOGP: The position information-based scheme and the oldest
points-based scheme. We apply our approach to learn the inverse dynamics of a
collaborative robot with 7 degrees of freedom under a two-segment trajectory
tracking problem with task switching. Both simulations and experiments have
shown that the proposed approach achieves better tracking accuracy and
predictive smoothness compared with the two general data deletion schemes.Comment: Submitted to 2022 IEEE/ASME International Conference on Advanced
Intelligent Mechatronic
Online Simultaneous Semi-Parametric Dynamics Model Learning
Accurate models of robots' dynamics are critical for control, stability,
motion optimization, and interaction. Semi-Parametric approaches to dynamics
learning combine physics-based Parametric models with unstructured
Non-Parametric regression with the hope to achieve both accuracy and
generalizablity. In this paper we highlight the non-stationary problem created
when attempting to adapt both Parametric and Non-Parametric components
simultaneously. We present a consistency transform designed to compensate for
this non-stationary effect, such that the contributions of both models can
adapt simultaneously without adversely affecting the performance of the
platform. Thus we are able to apply the Semi-Parametric learning approach for
continuous iterative online adaptation, without relying on batch or offline
updates. We validate the transform via a perfect virtual model as well as by
applying the overall system on a Kuka LWR IV manipulator. We demonstrate
improved tracking performance during online learning and show a clear
transference of contribution between the two components with a learning bias
towards the Parametric component.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
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this work in other work
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
Derivative-Free Online Learning of Inverse Dynamics Models
This paper discusses online algorithms for inverse dynamics modeling in robotics. Several model classes, including rigid body dynamics models, data-driven models and semiparametric models (which are 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 needs to be approximated resorting to numerical differentiation schemes, in this paper, a new 'derivative-free' (DF) framework is proposed, which 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 DF methods outperform existing methodologies