17 research outputs found
A New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human robot
interaction. Tools such as acceleration policies can naturally support the
realization of reactive, adaptive, and compliant robots. These tools require us
to model the system dynamics accurately -- a difficult task. The fundamental
problem remains that simulation and reality diverge--we do not know how to
accurately change a robot's state. Thus, recent research on improving inverse
dynamics models has been focused on making use of machine learning techniques.
Traditional learning techniques train on the actual realized accelerations,
instead of the policy's desired accelerations, which is an indirect data
source. Here we show how an additional training signal -- measured at the
desired accelerations -- can be derived from a feedback control signal. This
effectively creates a second data source for learning inverse dynamics models.
Furthermore, we show how both the traditional and this new data source, can be
used to train task-specific models of the inverse dynamics, when used
independently or combined. We analyze the use of both data sources in
simulation and demonstrate its effectiveness on a real-world robotic platform.
We show that our system incrementally improves the learned inverse dynamics
model, and when using both data sources combined converges more consistently
and faster.Comment: IROS 201
Learning coupled forward-inverse models with combined prediction errors
Challenging tasks in unstructured environments require robots to learn complex models. Given a large amount of information, learning multiple simple models can offer an efficient alternative to a monolithic complex network. Training multiple models—that is, learning their parameters and their responsibilities—has been shown to be prohibitively hard as optimization is prone to local minima. To efficiently learn multiple models for different contexts, we thus develop a new algorithm based on expectation maximization (EM). In contrast to comparable concepts, this algorithm trains multiple modules of paired forward-inverse models by using the prediction errors of both forward and inverse models simultaneously. In particular, we show that our method yields a substantial improvement over only considering the errors of the forward models on tasks where the inverse space contains multiple solution
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
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Data availability has dramatically increased in recent years, driving
model-based control methods to exploit learning techniques for improving the
system description, and thus control performance. Two key factors that hinder
the practical applicability of learning methods in control are their high
computational complexity and limited generalization capabilities to unseen
conditions. Meta-learning is a powerful tool that enables efficient learning
across a finite set of related tasks, easing adaptation to new unseen tasks.
This paper makes use of a meta-learning approach for adaptive model predictive
control, by learning a system model that leverages data from previous related
tasks, while enabling fast fine-tuning to the current task during closed-loop
operation. The dynamics is modeled via Gaussian process regression and,
building on the Karhunen-Lo{\`e}ve expansion, can be approximately reformulated
as a finite linear combination of kernel eigenfunctions. Using data collected
over a set of tasks, the eigenfunction hyperparameters are optimized in a
meta-training phase by maximizing a variational bound for the log-marginal
likelihood. During meta-testing, the eigenfunctions are fixed, so that only the
linear parameters are adapted to the new unseen task in an online adaptive
fashion via Bayesian linear regression, providing a simple and efficient
inference scheme. Simulation results are provided for autonomous racing with
miniature race cars adapting to unseen road conditions